diff --git a/.gitattributes b/.gitattributes
index a6344aac8c09253b3b630fb776ae94478aa0275b..46cb1f1311d77a3abce3cab3edea25077e53c8d1 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -33,3 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.splat filter=lfs diff=lfs merge=lfs -text
+*.glb filter=lfs diff=lfs merge=lfs -text
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..b6542f5205d3f25c9f5feee8881141f3e09947d8
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,2 @@
+__pycache__/
+.gradio/
\ No newline at end of file
diff --git a/app.py b/app.py
index 652dc459175b17dc4acd2394b8b8ad1edd2394a5..a314416f559603d37490f581fbe785bac19994ea 100644
--- a/app.py
+++ b/app.py
@@ -1,154 +1,1187 @@
 import gradio as gr
-import numpy as np
+from PIL import Image
+from pathlib import Path
+from omegaconf import DictConfig, OmegaConf
+from tqdm import tqdm, trange
 import random
-
-# import spaces #[uncomment to use ZeroGPU]
-from diffusers import DiffusionPipeline
+import math
+import hydra
+import numpy as np
 import torch
+import torch.nn as nn
+import torch.backends.cudnn
+import warp as wp
+import glob
+from torch.utils.data import DataLoader
+import os
+import time
+import cv2
+import copy
+import open3d as o3d
+import kornia
+import yaml
+import matplotlib.pyplot as plt
+from sklearn.neighbors import NearestNeighbors
+from dgl.geometry import farthest_point_sampler
+
+import sys
+sys.path.insert(0, str(Path(__file__).parent / "src"))
+sys.path.append(str(Path(__file__).parent / "src" / "experiments"))
+
+from pgnd.sim import Friction, CacheDiffSimWithFrictionBatch, StaticsBatch, CollidersBatch
+from pgnd.material import PGNDModel
+from pgnd.data import RealTeleopBatchDataset, RealGripperDataset
+from pgnd.utils import Logger, get_root, mkdir
+from pgnd.ffmpeg import make_video
+
+from real_world.utils.render_utils import interpolate_motions
+from real_world.gs.helpers import setup_camera
+from real_world.gs.convert import save_to_splat, read_splat
+
+from diff_gaussian_rasterization import GaussianRasterizer
+from diff_gaussian_rasterization import GaussianRasterizationSettings as Camera
+
+root = Path(__file__).parent / "src" / "experiments"
+
+
+def quat2mat(quat):
+    return kornia.geometry.conversions.quaternion_to_rotation_matrix(quat)
+
+
+def mat2quat(mat):
+    return kornia.geometry.conversions.rotation_matrix_to_quaternion(mat)
+
+
+def fps(x, enabled, n, device, random_start=False):
+    assert torch.diff(enabled * 1.0).sum() in [0.0, -1.0]
+    start_idx = random.randint(0, enabled.sum() - 1) if random_start else 0
+    fps_idx = farthest_point_sampler(x[enabled][None], n, start_idx=start_idx)[0]
+    fps_idx = fps_idx.to(x.device)
+    return fps_idx
+
+
+class DynamicsVisualizer:
+
+    def __init__(self):
+        wp.init()
+
+        self.width = 640
+        self.height = 480
+
+        best_models = {
+            'cloth': ['cloth', 'train', 100000, [610, 650]],
+            'rope': ['rope', 'train', 100000, [651, 691]],
+            'paperbag': ['paperbag', 'train', 100000, [200, 220]],
+            'sloth': ['sloth', 'train', 100000, [113, 133]],
+            'box': ['box', 'train', 100000, [306, 323]],
+            'bread': ['bread', 'train', 100000, [143, 163]],
+        }
+
+        task_name = 'rope'
+
+        with open(root / f'log/{best_models[task_name][0]}/{best_models[task_name][1]}/hydra.yaml', 'r') as f:
+            config = yaml.load(f, Loader=yaml.CLoader)
+        cfg = OmegaConf.create(config)
+
+        cfg.iteration = best_models[task_name][2]
+        cfg.start_episode = best_models[task_name][3][0]
+        cfg.end_episode = best_models[task_name][3][1]
+        cfg.sim.num_steps = 1000
+        cfg.sim.gripper_forcing = False
+        cfg.sim.uniform = True
+        cfg.sim.use_pv = False
+
+        device = torch.device('cuda:0')
+
+        self.cfg = cfg
+        self.device = device
+        self.k_rel = 8  # knn for relations
+        self.k_wgt = 16  # knn for weights
+        self.with_bg = True
+        self.render_gripper = True
+        self.verbose = False
+
+        self.dt_base = cfg.sim.dt
+        self.high_freq_pred = True
+
+        gpus = [int(gpu) for gpu in cfg.gpus]
+        wp_devices = [wp.get_device(f'cuda:{gpu}') for gpu in gpus]
+        torch_devices = [torch.device(f'cuda:{gpu}') for gpu in gpus]
+        device_count = len(torch_devices)
+        assert device_count == 1
+        self.wp_device = wp_devices[0]
+        self.torch_device = torch_devices[0]
+
+        seed = cfg.seed
+        random.seed(seed)
+        np.random.seed(seed)
+        torch.manual_seed(seed)
+        torch.autograd.set_detect_anomaly(True)
+        torch.backends.cudnn.benchmark = True
+
+        self.clear()
+    
+    def clear(self, clear_params=True):
+        self.metadata = {}
+        self.config = {}
+        if clear_params:
+            self.params = None
+            self.state = {
+                # object
+                'x': None,
+                'v': None,
+                'x_his': None,
+                'v_his': None,
+                'x_pred': None,
+                'v_pred': None,
+                'clip_bound': None,
+                'enabled': None,
+                # robot
+                'prev_key_pos': None,
+                'prev_key_pos_timestamp': None,
+                'sub_pos': None,  # filling in between key positions
+                'sub_pos_timestamps': None,
+                'gripper_radius': None,
+            }
+            self.preprocess_metadata = None
+            self.table_params = None
+            self.gripper_params = None
+
+        self.sim = None
+        self.statics = None
+        self.colliders = None
+        self.material = None
+        self.friction = None
+
+    def load_scaniverse(self, data_path):
+
+        ### load splat params
+
+        params_obj = read_splat(data_path / 'object.splat')
+        params_table = read_splat(data_path / 'table.splat')
+        params_robot = read_splat(data_path / 'gripper.splat')
+
+        pts, colors, scales, quats, opacities = params_obj
+        self.params = {
+            'means3D': torch.from_numpy(pts).to(torch.float32).to(self.device),
+            'rgb_colors': torch.from_numpy(colors).to(torch.float32).to(self.device),
+            'log_scales': torch.log(torch.from_numpy(scales).to(torch.float32).to(self.device)),
+            'unnorm_rotations': torch.from_numpy(quats).to(torch.float32).to(self.device),
+            'logit_opacities': torch.logit(torch.from_numpy(opacities).to(torch.float32).to(self.device))
+        }
+
+        t_pts, t_colors, t_scales, t_quats, t_opacities = params_table
+        t_pts = torch.tensor(t_pts).to(torch.float32).to(self.device)
+        t_colors = torch.tensor(t_colors).to(torch.float32).to(self.device)
+        t_scales = torch.tensor(t_scales).to(torch.float32).to(self.device)
+        t_quats = torch.tensor(t_quats).to(torch.float32).to(self.device)
+        t_opacities = torch.tensor(t_opacities).to(torch.float32).to(self.device)
+
+        g_pts, g_colors, g_scales, g_quats, g_opacities = params_robot
+        g_pts = torch.tensor(g_pts).to(torch.float32).to(self.device)
+        g_colors = torch.tensor(g_colors).to(torch.float32).to(self.device)
+        g_scales = torch.tensor(g_scales).to(torch.float32).to(self.device)
+        g_quats = torch.tensor(g_quats).to(torch.float32).to(self.device)
+        g_opacities = torch.tensor(g_opacities).to(torch.float32).to(self.device)
+
+        self.table_params = t_pts, t_colors, t_scales, t_quats, t_opacities  # data frame
+        self.gripper_params = g_pts, g_colors, g_scales, g_quats, g_opacities  # data frame
+
+        n_particles = self.cfg.sim.n_particles
+        self.state['clip_bound'] = torch.tensor([self.cfg.model.clip_bound], dtype=torch.float32)
+        self.state['enabled'] = torch.ones(n_particles, dtype=torch.bool)
+
+        ### load preprocess metadata
+
+        cfg = self.cfg
+        dx = cfg.sim.num_grids[-1]
+
+        p_x = torch.tensor(pts).to(torch.float32).to(self.device)
+        R = torch.tensor(
+            [[1, 0, 0],
+            [0, 0, -1],
+            [0, 1, 0]]
+        ).to(p_x.device).to(p_x.dtype)
+        p_x_rotated = p_x @ R.T
+
+        scale = 1.0
+        p_x_rotated_scaled = p_x_rotated * scale
+
+        global_translation = torch.tensor([
+            0.5 - p_x_rotated_scaled[:, 0].mean(),
+            dx * (cfg.model.clip_bound + 0.5) - p_x_rotated_scaled[:, 1].min(),
+            0.5 - p_x_rotated_scaled[:, 2].mean(),
+        ], dtype=p_x_rotated_scaled.dtype, device=p_x_rotated_scaled.device)
+
+        R_viewer = torch.tensor(
+            [[1, 0, 0],
+            [0, 0, -1],
+            [0, 1, 0]]
+        ).to(p_x.device).to(p_x.dtype)
+        t_viewer = torch.tensor([0, 0, 0]).to(p_x.device).to(p_x.dtype)
+
+        self.preprocess_metadata = {
+            'R': R,
+            'R_viewer': R_viewer,
+            't_viewer': t_viewer,
+            'scale': scale,
+            'global_translation': global_translation,
+        }
+
+        ### load eef
+        grippers = np.loadtxt(data_path / 'eef_xyz.txt')[None]
+        assert grippers.shape == (1, 3)
+        
+        if grippers is not None:
+            grippers = torch.tensor(grippers).to(self.device).to(torch.float32)
+
+            # transform
+            # data frame to model frame
+            R = self.preprocess_metadata['R']
+            scale = self.preprocess_metadata['scale']
+            global_translation = self.preprocess_metadata['global_translation']
+            grippers[:, :3] = grippers[:, :3] @ R.T
+            grippers[:, :3] = grippers[:, :3] * scale
+            grippers[:, :3] += global_translation
+
+            assert grippers.shape[0] == 1
+            self.state['prev_key_pos'] = grippers[:, :3]  # (1, 3)
+            # self.state['prev_key_pos_timestamp'] = torch.zeros(1).to(self.device).to(torch.float32)
+            self.state['gripper_radius'] = cfg.model.gripper_radius
+
+
+    def load_params(self, params_path, remove_low_opa=True, remove_black=False):
+        pts, colors, scales, quats, opacities = read_splat(params_path)
+        
+        if remove_low_opa:
+            low_opa_idx = opacities[:, 0] < 0.1
+            pts = pts[~low_opa_idx]
+            colors = colors[~low_opa_idx]
+            quats = quats[~low_opa_idx]
+            opacities = opacities[~low_opa_idx]
+            scales = scales[~low_opa_idx]
+
+        if remove_black:
+            low_color_idx = colors.sum(axis=-1) < 0.5
+            pts = pts[~low_color_idx]
+            colors = colors[~low_color_idx]
+            quats = quats[~low_color_idx]
+            opacities = opacities[~low_color_idx]
+            scales = scales[~low_color_idx]
+        
+        self.params = {
+            'means3D': torch.from_numpy(pts).to(torch.float32).to(self.device),
+            'rgb_colors': torch.from_numpy(colors).to(torch.float32).to(self.device),
+            'log_scales': torch.log(torch.from_numpy(scales).to(torch.float32).to(self.device)),
+            'unnorm_rotations': torch.from_numpy(quats).to(torch.float32).to(self.device),
+            'logit_opacities': torch.logit(torch.from_numpy(opacities).to(torch.float32).to(self.device))
+        }
+
+        table_splat = root / 'log/gs/ckpts/table.splat'
+        sphere_splat = root / 'log/gs/ckpts/sphere.splat'
+        gripper_splat = root / 'log/gs/ckpts/gripper.splat'  # gripper_new.splat
+
+        table_params = read_splat(table_splat)  # numpy
+
+        ## add table and gripper
+        # add table
+        t_pts, t_colors, t_scales, t_quats, t_opacities = table_params
+        t_pts = torch.tensor(t_pts).to(torch.float32).to(self.device)
+        t_colors = torch.tensor(t_colors).to(torch.float32).to(self.device)
+        t_scales = torch.tensor(t_scales).to(torch.float32).to(self.device)
+        t_quats = torch.tensor(t_quats).to(torch.float32).to(self.device)
+        t_opacities = torch.tensor(t_opacities).to(torch.float32).to(self.device)
+
+        # add table pos
+        t_pts = t_pts + torch.tensor([0, 0, 0.02]).to(torch.float32).to(self.device)
+
+        # add gripper
+        gripper_params = read_splat(gripper_splat)  # numpy
+        
+        g_pts, g_colors, g_scales, g_quats, g_opacities = gripper_params
+        g_pts = torch.tensor(g_pts).to(torch.float32).to(self.device)
+        g_colors = torch.tensor(g_colors).to(torch.float32).to(self.device)
+        g_scales = torch.tensor(g_scales).to(torch.float32).to(self.device)
+        g_quats = torch.tensor(g_quats).to(torch.float32).to(self.device)
+        g_opacities = torch.tensor(g_opacities).to(torch.float32).to(self.device)
+
+        # we do not do the gripper translation now because this will center the gripper in the data frame but not the viewer frame
+
+        self.table_params = t_pts, t_colors, t_scales, t_quats, t_opacities  # data frame
+        self.gripper_params = g_pts, g_colors, g_scales, g_quats, g_opacities  # data frame
+
+        # load other info
+        n_particles = self.cfg.sim.n_particles
+        self.state['clip_bound'] = torch.tensor([self.cfg.model.clip_bound], dtype=torch.float32)
+        self.state['enabled'] = torch.ones(n_particles, dtype=torch.bool)
+
+
+    def set_camera(self, w, h, intr, w2c=None, R=None, t=None, near=0.01, far=100.0):
+        if w2c is None:
+            assert R is not None and t is not None
+            w2c = Rt_to_w2c(R, t)
+        self.metadata = {
+            'w': w,
+            'h': h,
+            'k': intr,
+            'w2c': w2c,
+        }
+        self.config = {'near': near, 'far': far}
+    
+    def load_eef(self, grippers=None, eef_t=None):
+        assert self.state['prev_key_pos'] is None
+
+        if grippers is not None:
+            grippers = torch.tensor(grippers).to(self.device).to(torch.float32)
+            eef_t = torch.tensor(eef_t).to(self.device).to(torch.float32)
+            grippers[:, :3] = grippers[:, :3] + eef_t
+
+            # transform
+            # data frame to model frame
+            R = self.preprocess_metadata['R']
+            scale = self.preprocess_metadata['scale']
+            global_translation = self.preprocess_metadata['global_translation']
+            grippers[:, :3] = grippers[:, :3] @ R.T
+            grippers[:, :3] = grippers[:, :3] * scale
+            grippers[:, :3] += global_translation
+
+            assert grippers.shape[0] == 1
+            self.state['prev_key_pos'] = grippers[:, :3]  # (1, 3)
+            # self.state['prev_key_pos_timestamp'] = torch.zeros(1).to(self.device).to(torch.float32) + 0.001
+            self.state['gripper_radius'] = self.cfg.model.gripper_radius
+
+    def load_preprocess_metadata(self, p_x_orig):
+        cfg = self.cfg
+        dx = cfg.sim.num_grids[-1]
+
+        p_x_orig = p_x_orig.to(self.device)
+        R = torch.tensor(
+            [[1, 0, 0],
+            [0, 0, -1],
+            [0, 1, 0]]
+        ).to(p_x_orig.device).to(p_x_orig.dtype)
+        p_x_orig_rotated = torch.einsum('nij,jk->nik', p_x_orig, R.T)
+
+        scale = 1.0
+        p_x_orig_rotated_scaled = p_x_orig_rotated * scale
+
+        global_translation = torch.tensor([
+            0.5 - p_x_orig_rotated_scaled[:, :, 0].mean(),
+            dx * (cfg.model.clip_bound + 0.5) - p_x_orig_rotated_scaled[:, :, 1].min(),
+            0.5 - p_x_orig_rotated_scaled[:, :, 2].mean(),
+        ], dtype=p_x_orig_rotated_scaled.dtype, device=p_x_orig_rotated_scaled.device)
+
+        R_viewer = torch.tensor(
+            [[1, 0, 0],
+            [0, 0, -1],
+            [0, 1, 0]]
+        ).to(p_x_orig.device).to(p_x_orig.dtype)
+        t_viewer = torch.tensor([0, 0, 0]).to(p_x_orig.device).to(p_x_orig.dtype)
+
+        self.preprocess_metadata = {
+            'R': R,
+            'R_viewer': R_viewer,
+            't_viewer': t_viewer,
+            'scale': scale,
+            'global_translation': global_translation,
+        }
+
+    @torch.no_grad
+    def render(self, render_data, cam_id, bg=[0.7, 0.7, 0.7]):
+        render_data = {k: v.to(self.device) for k, v in render_data.items()}
+        w, h = self.metadata['w'], self.metadata['h']
+        k, w2c = self.metadata['k'], self.metadata['w2c']
+        cam = setup_camera(w, h, k, w2c, self.config['near'], self.config['far'], bg)
+        im, _, depth, = GaussianRasterizer(raster_settings=cam)(**render_data)
+        return im, depth
+    
+    def knn_relations(self, bones):
+        k = self.k_rel
+        knn = NearestNeighbors(n_neighbors=k+1, algorithm='kd_tree').fit(bones.detach().cpu().numpy())
+        _, indices = knn.kneighbors(bones.detach().cpu().numpy())  # (N, k)
+        indices = indices[:, 1:]  # exclude self
+        return indices
+    
+    def knn_weights_brute(self, bones, pts):
+        k = self.k_wgt
+        dist = torch.norm(pts[:, None] - bones, dim=-1)  # (n_pts, n_bones)
+        _, indices = torch.topk(dist, k, dim=-1, largest=False)
+        bones_selected = bones[indices]  # (N, k, 3)
+        dist = torch.norm(bones_selected - pts[:, None], dim=-1)  # (N, k)
+        weights = 1 / (dist + 1e-6)
+        weights = weights / weights.sum(dim=-1, keepdim=True)  # (N, k)
+        weights_all = torch.zeros((pts.shape[0], bones.shape[0]), device=pts.device)
+        weights_all[torch.arange(pts.shape[0])[:, None], indices] = weights
+        return weights_all
+    
+    def update_camera(self, k, w2c, w=None, h=None, near=0.01, far=100.0):
+        self.metadata['k'] = k
+        self.metadata['w2c'] = w2c
+        if w is not None:
+            self.metadata['w'] = w
+        if h is not None:
+            self.metadata['h'] = h
+        self.config['near'] = near
+        self.config['far'] = far
+    
+    def init_model(self, batch_size, num_steps, num_particles, ckpt_path=None):
+        self.cfg.sim.num_steps = num_steps
+        cfg = self.cfg
+
+        sim = CacheDiffSimWithFrictionBatch(cfg, num_steps, batch_size, self.wp_device, requires_grad=True)
+
+        statics = StaticsBatch()
+        statics.init(shape=(batch_size, num_particles), device=self.wp_device)
+        statics.update_clip_bound(self.state['clip_bound'])
+        statics.update_enabled(self.state['enabled'][None])
+        colliders = CollidersBatch()
+        colliders.init(shape=(batch_size, cfg.sim.num_grippers), device=self.wp_device)
+
+        self.sim = sim
+        self.statics = statics
+        self.colliders = colliders
+        
+        # load ckpt
+        ckpt_path = root / 'log/rope/train/ckpt/100000.pt'
+        ckpt = torch.load(ckpt_path, map_location=self.torch_device)
+        
+        material: nn.Module = PGNDModel(cfg)
+        material.to(self.torch_device)
+        material.load_state_dict(ckpt['material'])
+        material.requires_grad_(False)
+        material.eval()
+        
+        if 'friction' in ckpt:
+            friction = ckpt['friction']['mu'].reshape(-1, 1)
+        else:
+            friction = torch.tensor(cfg.model.friction.value, device=self.torch_device).reshape(-1, 1)
+        
+        self.material = material
+        self.friction = friction
+
+    def reload_model(self, num_steps):  # only change num_steps
+        self.cfg.sim.num_steps = num_steps
+        sim = CacheDiffSimWithFrictionBatch(self.cfg, num_steps, 1, self.wp_device, requires_grad=True)
+        self.sim = sim
+
+    @torch.no_grad
+    def step(self):
+        cfg = self.cfg
+        batch_size = 1
+        num_steps = 1
+        num_particles = cfg.sim.n_particles
+
+        # update state by previous prediction
+        self.state['x_his'] = torch.cat([self.state['x_his'][1:], self.state['x'][None]], dim=0)
+        self.state['v_his'] = torch.cat([self.state['v_his'][1:], self.state['v'][None]], dim=0)
+        self.state['x'] = self.state['x_pred'].clone()
+        self.state['v'] = self.state['v_pred'].clone()
+        
+        eef_xyz_key = self.state['prev_key_pos']  # (1, 3), model frame
+        eef_xyz_sub = self.state['sub_pos']  # (T, 1, 3), model frame
+        
+        if eef_xyz_sub is None:
+            return
+
+        # eef_xyz_key_timestamp = self.state['prev_key_pos_timestamp']
+        # eef_xyz_sub_timestamps = self.state['sub_pos_timestamps']
+        
+        # assert eef_xyz_key_timestamp.item() > 0
+
+        # delta_t = (eef_xyz_sub_timestamps[-1] - eef_xyz_key_timestamp).item()
+
+        # if (not self.high_freq_pred) and delta_t < self.dt_base * 0.9:
+        #     return
+        # cfg.sim.dt = delta_t
+
+        eef_xyz_key_next = eef_xyz_sub[-1]  # (1, 3), model frame
+        eef_v = (eef_xyz_key_next - eef_xyz_key) / cfg.sim.dt
+        if self.verbose:
+            print('delta_t:', np.round(cfg.sim.dt, 4))
+            print('eef_xyz_key_next:', eef_xyz_key_next.cpu().numpy().tolist())
+            print('eef_xyz_key:', eef_xyz_key.cpu().numpy().tolist())
+            print('v:', eef_v.cpu().numpy().tolist())
+
+        # load model, sim, statics, colliders
+        self.reload_model(num_steps)
+        
+        # initialize colliders
+        if cfg.sim.num_grippers > 0:
+            grippers = torch.zeros((batch_size, cfg.sim.num_grippers, 15), device=self.torch_device)
+            eef_quat = torch.tensor([1, 0, 0, 0], dtype=torch.float32, device=self.torch_device).repeat(batch_size, cfg.sim.num_grippers, 1)  # (B, G, 4)
+            eef_quat_vel = torch.zeros((batch_size, cfg.sim.num_grippers, 3), dtype=torch.float32, device=self.torch_device)
+            eef_gripper = torch.zeros((batch_size, cfg.sim.num_grippers), dtype=torch.float32, device=self.torch_device)
+            grippers[:, :, :3] = eef_xyz_key
+            grippers[:, :, 3:6] = eef_v
+            grippers[:, :, 6:10] = eef_quat
+            grippers[:, :, 10:13] = eef_quat_vel
+            grippers[:, :, 13] = cfg.model.gripper_radius
+            grippers[:, :, 14] = eef_gripper
+            self.colliders.initialize_grippers(grippers)
+
+        x = self.state['x'].clone()[None].repeat(batch_size, 1, 1)
+        v = self.state['v'].clone()[None].repeat(batch_size, 1, 1)
+        x_his = self.state['x_his'].permute(1, 0, 2).clone()
+        assert x_his.shape[0] == num_particles
+        x_his = x_his.reshape(num_particles, -1)[None].repeat(batch_size, 1, 1)
+        v_his = self.state['v_his'].permute(1, 0, 2).clone()
+        assert v_his.shape[0] == num_particles
+        v_his = v_his.reshape(num_particles, -1)[None].repeat(batch_size, 1, 1)
+        enabled = self.state['enabled'].clone().to(self.torch_device)[None].repeat(batch_size, 1)
+
+        for t in range(num_steps):
+            x_in = x.clone()
+            pred = self.material(x, v, x_his, v_his, enabled)
+            
+            # x_his = torch.cat([x_his.reshape(batch_size, num_particles, -1, 3)[:, :, 1:], x[:, :, None].detach()], dim=2)
+            # v_his = torch.cat([v_his.reshape(batch_size, num_particles, -1, 3)[:, :, 1:], v[:, :, None].detach()], dim=2)
+            # x_his = x_his.reshape(batch_size, num_particles, -1)
+            # v_his = v_his.reshape(batch_size, num_particles, -1)
+            
+            x, v = self.sim(self.statics, self.colliders, t, x, v, self.friction, pred)
+
+        # calculate new x_pred, v_pred, eef_xyz_key and eef_xyz_sub
+        x_pred = x[0].clone()
+        v_pred = v[0].clone()
+        self.state['x_pred'] = x_pred
+        self.state['v_pred'] = v_pred
+        # self.state['x_his'] = x_his[0].reshape(num_particles, self.cfg.sim.n_history, 3).permute(1, 0, 2).clone()
+        # self.state['v_his'] = v_his[0].reshape(num_particles, self.cfg.sim.n_history, 3).permute(1, 0, 2).clone()
+
+        self.state['prev_key_pos'] = eef_xyz_key_next
+        # self.state['prev_key_pos_timestamp'] = eef_xyz_sub_timestamps[-1]
+        self.state['sub_pos'] = None
+        # self.state['sub_pos_timestamps'] = None
+
+    def preprocess_x(self, p_x):  # viewer frame to model frame (not data frame)
+        R = self.preprocess_metadata['R']
+        R_viewer = self.preprocess_metadata['R_viewer']
+        t_viewer = self.preprocess_metadata['t_viewer']
+        scale = self.preprocess_metadata['scale']
+        global_translation = self.preprocess_metadata['global_translation']
+
+        # viewer frame to model frame
+        p_x = (p_x - t_viewer) @ R_viewer
+
+        # model frame to data frame
+        # p_x -= global_translation
+        # p_x = p_x / scale
+        # p_x = p_x @ torch.linalg.inv(R).T
+
+        return p_x
+    
+    def preprocess_gripper(self, grippers):  # viewer frame to model frame (not data frame)
+        R = self.preprocess_metadata['R']
+        R_viewer = self.preprocess_metadata['R_viewer']
+        t_viewer = self.preprocess_metadata['t_viewer']
+        scale = self.preprocess_metadata['scale']
+        global_translation = self.preprocess_metadata['global_translation']
 
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model_repo_id = "stabilityai/sdxl-turbo"  # Replace to the model you would like to use
-
-if torch.cuda.is_available():
-    torch_dtype = torch.float16
-else:
-    torch_dtype = torch.float32
-
-pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
-pipe = pipe.to(device)
-
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
-
-
-# @spaces.GPU #[uncomment to use ZeroGPU]
-def infer(
-    prompt,
-    negative_prompt,
-    seed,
-    randomize_seed,
-    width,
-    height,
-    guidance_scale,
-    num_inference_steps,
-    progress=gr.Progress(track_tqdm=True),
-):
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-
-    generator = torch.Generator().manual_seed(seed)
-
-    image = pipe(
-        prompt=prompt,
-        negative_prompt=negative_prompt,
-        guidance_scale=guidance_scale,
-        num_inference_steps=num_inference_steps,
-        width=width,
-        height=height,
-        generator=generator,
-    ).images[0]
-
-    return image, seed
-
-
-examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
-]
-
-css = """
-#col-container {
-    margin: 0 auto;
-    max-width: 640px;
-}
-"""
-
-with gr.Blocks(css=css) as demo:
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(" # Text-to-Image Gradio Template")
-
-        with gr.Row():
-            prompt = gr.Text(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
-            )
-
-            run_button = gr.Button("Run", scale=0, variant="primary")
-
-        result = gr.Image(label="Result", show_label=False)
-
-        with gr.Accordion("Advanced Settings", open=False):
-            negative_prompt = gr.Text(
-                label="Negative prompt",
-                max_lines=1,
-                placeholder="Enter a negative prompt",
-                visible=False,
-            )
-
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=0,
-            )
-
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+        # viewer frame to model frame
+        grippers[:, :3] = grippers[:, :3] @ R_viewer
+
+        return grippers
+    
+    def inverse_preprocess_x(self, p_x):  # model frame (not data frame) to viewer frame
+        R = self.preprocess_metadata['R']
+        R_viewer = self.preprocess_metadata['R_viewer']
+        t_viewer = self.preprocess_metadata['t_viewer']
+        scale = self.preprocess_metadata['scale']
+        global_translation = self.preprocess_metadata['global_translation']
+
+        # model frame to viewer frame
+        p_x = p_x @ R_viewer.T + t_viewer
+
+        return p_x
+    
+    def inverse_preprocess_gripper(self, grippers):  # model frame (not data frame) to viewer frame
+        R = self.preprocess_metadata['R']
+        R_viewer = self.preprocess_metadata['R_viewer']
+        t_viewer = self.preprocess_metadata['t_viewer']
+        scale = self.preprocess_metadata['scale']
+        global_translation = self.preprocess_metadata['global_translation']
+
+        # model frame to viewer frame
+        grippers[:, :3] = grippers[:, :3] @ R_viewer.T + t_viewer
+
+        return grippers
+
+    def preprocess_gs(self, params):
+        if isinstance(params, dict):
+            xyz = params['means3D']
+            rgb = params['rgb_colors']
+            quat = torch.nn.functional.normalize(params['unnorm_rotations'])
+            opa = torch.sigmoid(params['logit_opacities'])
+            scales = torch.exp(params['log_scales'])
+        else:
+            assert isinstance(params, tuple)
+            xyz, rgb, quat, opa, scales = params
+        
+        quat = torch.nn.functional.normalize(quat, dim=-1)
+
+        # transform
+        R = self.preprocess_metadata['R']
+        R_viewer = self.preprocess_metadata['R_viewer']
+        scale = self.preprocess_metadata['scale']
+        global_translation = self.preprocess_metadata['global_translation']
+
+        mat = quat2mat(quat)
+        mat = R @ mat
+        xyz = xyz @ R.T
+        xyz = xyz * scale
+        xyz += global_translation
+        quat = mat2quat(mat)
+        scales = scales * scale
+
+        # viewer-specific transform (flip y and z)
+        # model frame to viewer frame
+        xyz = xyz @ R_viewer.T
+        quat = mat2quat(R_viewer @ quat2mat(quat))
+
+        t_viewer = -xyz.mean(dim=0)
+        t_viewer[2] = 0
+        xyz += t_viewer
+        print('Overwriting t_viewer to be the planar mean of the object')
+        self.preprocess_metadata['t_viewer'] = t_viewer
+
+        if isinstance(params, dict):
+            params['means3D'] = xyz
+            params['rgb_colors'] = rgb
+            params['unnorm_rotations'] = quat
+            params['logit_opacities'] = opa
+            params['log_scales'] = torch.log(scales)
+        else:
+            params = xyz, rgb, quat, opa, scales
+        
+        return params
+
+    def preprocess_gs(self, params):
+        if isinstance(params, dict):
+            xyz = params['means3D']
+            rgb = params['rgb_colors']
+            quat = torch.nn.functional.normalize(params['unnorm_rotations'])
+            opa = torch.sigmoid(params['logit_opacities'])
+            scales = torch.exp(params['log_scales'])
+        else:
+            assert isinstance(params, tuple)
+            xyz, rgb, quat, opa, scales = params
+        
+        quat = torch.nn.functional.normalize(quat, dim=-1)
+
+        # transform
+        R = self.preprocess_metadata['R']
+        R_viewer = self.preprocess_metadata['R_viewer']
+        scale = self.preprocess_metadata['scale']
+        global_translation = self.preprocess_metadata['global_translation']
+
+        mat = quat2mat(quat)
+        mat = R @ mat
+        xyz = xyz @ R.T
+        xyz = xyz * scale
+        xyz += global_translation
+        quat = mat2quat(mat)
+        scales = scales * scale
+
+        # viewer-specific transform (flip y and z)
+        # model frame to viewer frame
+        xyz = xyz @ R_viewer.T
+        quat = mat2quat(R_viewer @ quat2mat(quat))
+
+        t_viewer = -xyz.mean(dim=0)
+        t_viewer[2] = 0
+        xyz += t_viewer
+        print('Overwriting t_viewer to be the planar mean of the object')
+        self.preprocess_metadata['t_viewer'] = t_viewer
+
+        if isinstance(params, dict):
+            params['means3D'] = xyz
+            params['rgb_colors'] = rgb
+            params['unnorm_rotations'] = quat
+            params['logit_opacities'] = opa
+            params['log_scales'] = torch.log(scales)
+        else:
+            params = xyz, rgb, quat, opa, scales
+        
+        return params
+    
+    def preprocess_bg_gs(self):
+        t_pts, t_colors, t_scales, t_quats, t_opacities = self.table_params
+        g_pts, g_colors, g_scales, g_quats, g_opacities = self.gripper_params
+        
+        # identify tip first
+        g_pts_tip_z = g_pts[:, 2].max()
+        g_pts_tip_mask = (g_pts[:, 2] > g_pts_tip_z - 0.04) & (g_pts[:, 2] < g_pts_tip_z)
+        
+        R = self.preprocess_metadata['R']
+        R_viewer = self.preprocess_metadata['R_viewer']
+        t_viewer = self.preprocess_metadata['t_viewer']
+        scale = self.preprocess_metadata['scale']
+        global_translation = self.preprocess_metadata['global_translation']
+
+        t_mat = quat2mat(t_quats)
+        t_mat = R @ t_mat
+        t_pts = t_pts @ R.T
+        t_pts = t_pts * scale
+        t_pts += global_translation
+        t_quats = mat2quat(t_mat)
+        t_scales = t_scales * scale
+
+        t_pts = t_pts @ R_viewer.T
+        t_quats = mat2quat(R_viewer @ quat2mat(t_quats))
+        t_pts += t_viewer
+
+        g_mat = quat2mat(g_quats)
+        g_mat = R @ g_mat
+        g_pts = g_pts @ R.T
+        g_pts = g_pts * scale
+        g_pts += global_translation
+        g_quats = mat2quat(g_mat)
+        g_scales = g_scales * scale
+        
+        g_pts = g_pts @ R_viewer.T
+        g_quats = mat2quat(R_viewer @ quat2mat(g_quats))
+        g_pts += t_viewer
+
+        # TODO: center gripper in the viewer frame
+        g_pts_tip = g_pts[g_pts_tip_mask]
+        g_pts_tip_mean_xy = g_pts_tip[:, :2].mean(dim=0)
+        g_pts_translation = torch.tensor([-g_pts_tip_mean_xy[0], -g_pts_tip_mean_xy[1], -0.23]).to(torch.float32).to(self.device)
+        g_pts = g_pts + g_pts_translation
+
+        self.table_params = t_pts, t_colors, t_scales, t_quats, t_opacities
+        self.gripper_params = g_pts, g_colors, g_scales, g_quats, g_opacities
+
+    def update_rendervar(self, rendervar):
+        p_x = self.state['x']
+        p_x_viewer = self.inverse_preprocess_x(p_x)
+
+        p_x_pred = self.state['x_pred']
+        p_x_pred_viewer = self.inverse_preprocess_x(p_x_pred)
+
+        xyz = rendervar['means3D']
+        rgb = rendervar['colors_precomp']
+        quat = rendervar['rotations']
+        opa = rendervar['opacities']
+        scales = rendervar['scales']
+
+        relations = self.knn_relations(p_x_viewer)
+        weights = self.knn_weights_brute(p_x_viewer, xyz)
+        xyz, quat, _ = interpolate_motions(
+            bones=p_x_viewer,
+            motions=p_x_pred_viewer - p_x_viewer,
+            relations=relations,
+            weights=weights,
+            xyz=xyz,
+            quat=quat,
+        )
+
+        # normalize
+        quat = torch.nn.functional.normalize(quat, dim=-1)
+
+        rendervar = {
+            'means3D': xyz,
+            'colors_precomp': rgb,
+            'rotations': quat,
+            'opacities': opa,
+            'scales': scales,
+            'means2D': torch.zeros_like(xyz),
+        }
+
+        if self.with_bg:
+            t_pts, t_colors, t_scales, t_quats, t_opacities = self.table_params
+            
+            # merge
+            xyz = torch.cat([xyz, t_pts], dim=0)
+            rgb = torch.cat([rgb, t_colors], dim=0)
+            quat = torch.cat([quat, t_quats], dim=0)
+            opa = torch.cat([opa, t_opacities], dim=0)
+            scales = torch.cat([scales, t_scales], dim=0)
+
+            if self.render_gripper:
+                g_pts, g_colors, g_scales, g_quats, g_opacities = self.gripper_params
+
+                # add gripper pos
+                g_pts = g_pts + self.inverse_preprocess_gripper(self.state['prev_key_pos'][None].clone())[0]
+                
+                # merge
+                xyz = torch.cat([xyz, g_pts], dim=0)
+                rgb = torch.cat([rgb, g_colors], dim=0)
+                quat = torch.cat([quat, g_quats], dim=0)
+                opa = torch.cat([opa, g_opacities], dim=0)
+                scales = torch.cat([scales, g_scales], dim=0)
+
+            # normalize
+            quat = torch.nn.functional.normalize(quat, dim=-1)
+
+            rendervar_full = {
+                'means3D': xyz,
+                'colors_precomp': rgb,
+                'rotations': quat,
+                'opacities': opa,
+                'scales': scales,
+                'means2D': torch.zeros_like(xyz),
+            }
+        
+        else:
+            rendervar_full = rendervar
+
+        return rendervar, rendervar_full
+
+    def reset_state(self, params, visualize_image=False, init=False):
+        xyz_0 = params['means3D']
+        rgb_0 = params['rgb_colors']
+        quat_0 = torch.nn.functional.normalize(params['unnorm_rotations'])
+        opa_0 = torch.sigmoid(params['logit_opacities'])
+        scales_0 = torch.exp(params['log_scales'])
+
+        rendervar_init = {
+            'means3D': xyz_0,  
+            'colors_precomp': rgb_0,
+            'rotations': quat_0,
+            'opacities': opa_0,
+            'scales': scales_0,
+            'means2D': torch.zeros_like(xyz_0),
+        }  # before preprocess
+
+        w = self.width
+        h = self.height
+        center = (0, 0, 0.1)
+        distance = 0.7
+        elevation = 20
+        azimuth = 180.0
+        target = np.array(center)
+        theta = 90 + azimuth
+        z = distance * math.sin(math.radians(elevation))
+        y = math.cos(math.radians(theta)) * distance * math.cos(math.radians(elevation))
+        x = math.sin(math.radians(theta)) * distance * math.cos(math.radians(elevation))
+        origin = target + np.array([x, y, z])
+        
+        look_at = target - origin
+        look_at /= np.linalg.norm(look_at)
+        up = np.array([0.0, 0.0, 1.0])
+        right = np.cross(look_at, up)
+        right /= np.linalg.norm(right)
+        up = np.cross(right, look_at)
+        w2c = np.eye(4)
+        w2c[:3, 0] = right
+        w2c[:3, 1] = -up
+        w2c[:3, 2] = look_at
+        w2c[:3, 3] = origin
+        w2c = np.linalg.inv(w2c)
+
+        k = np.array(
+            [[w / 2 * 1.0, 0., w / 2],
+            [0., w / 2 * 1.0, h / 2],
+            [0., 0., 1.]],
+        )
+        self.update_camera(k, w2c, w, h)
+
+        n_particles = self.cfg.sim.n_particles
+        downsample_indices = fps(xyz_0, torch.ones_like(xyz_0[:, 0]).to(torch.bool), n_particles, self.torch_device)
+        p_x_viewer = xyz_0[downsample_indices]
+        p_x = self.preprocess_x(p_x_viewer)
+
+        self.state['x'] = p_x
+        self.state['v'] = torch.zeros_like(p_x)
+        self.state['x_his'] = p_x[None].repeat(self.cfg.sim.n_history, 1, 1)
+        self.state['v_his'] = torch.zeros_like(p_x[None].repeat(self.cfg.sim.n_history, 1, 1))
+        self.state['x_pred'] = p_x
+        self.state['v_pred'] = torch.zeros_like(p_x)
+
+        rendervar_init, rendervar_init_full = self.update_rendervar(rendervar_init)
+        im, depth = self.render(rendervar_init_full, 0, bg=[0.0, 0.0, 0.0])
+        im_vis = (im.permute(1, 2, 0) * 255.0).cpu().numpy().astype(np.uint8)
+
+        return rendervar_init
+
+
+
+
+
+
+
+    def reset(self):
+        params = self.preprocess_gs(self.params)
+        if self.with_bg:
+            self.preprocess_bg_gs()
+        rendervar = self.reset_state(params, visualize_image=False, init=True)
+        rendervar, rendervar_full = self.update_rendervar(rendervar)
+        self.rendervar = rendervar
+
+        im, depth = self.render(rendervar_full, 0, bg=[0.0, 0.0, 0.0])
+        im_show = (im.permute(1, 2, 0) * 255.0).cpu().numpy().astype(np.uint8).copy()
+
+        cv2.imwrite(str(root / 'log/temp_init/0000.png'), cv2.cvtColor(im_show, cv2.COLOR_RGB2BGR))
+
+        make_video(root / 'log/temp_init', root / f'log/gs/temp/form_video_init.mp4', '%04d.png', 1)
+
+        gs_pred = save_to_splat(
+            rendervar_full['means3D'].cpu().numpy(),
+            rendervar_full['colors_precomp'].cpu().numpy(),
+            rendervar_full['scales'].cpu().numpy(),
+            rendervar_full['rotations'].cpu().numpy(),
+            rendervar_full['opacities'].cpu().numpy(),
+            root / 'log/gs/temp/gs_pred.splat',
+            rot_rev=True,
+        )
+
+        form_video = gr.Video(
+            label='Predicted video',
+            value=root / f'log/gs/temp/form_video.mp4',
+            format='mp4',
+            width=self.width,
+            height=self.height,
+        )
+        form_3dgs_pred = gr.Model3D(
+            label='Predicted Gaussian Splats',
+            height=self.height,
+            value=root / 'log/gs/temp/gs_pred.splat',
+            clear_color=[0, 0, 0, 0],
+        )
+
+        return form_video, form_3dgs_pred
+
+    def run_command(self, unit_command):
+
+        os.system('rm -rf ' + str(root / 'log/temp/*'))
+
+        # im_list = []
+        for i in range(15):
+            dt = 0.1  # 100ms
+            command = torch.tensor([unit_command]).to(self.device).to(torch.float32)  # 5cm/s
+            command = self.preprocess_gripper(command)
+            # command_timestamp = torch.tensor([self.state['prev_key_pos_timestamp'] + (i+1) * dt]).to(self.device).to(torch.float32)
+            # print(command_timestamp)
+            if self.verbose:
+                print('command:', command.cpu().numpy().tolist())
+            
+            assert self.state['sub_pos'] is None
+
+            if self.state['sub_pos'] is None:
+                eef_xyz_latest = self.state['prev_key_pos']
+                # eef_xyz_timestamp_latest = self.state['prev_key_pos_timestamp']
+            
+            else:
+                eef_xyz_latest = self.state['sub_pos'][-1]  # (1, 3), model frame
+                # eef_xyz_timestamp_latest = self.state['sub_pos_timestamps'][-1].item()
+
+            eef_xyz_updated = eef_xyz_latest + command * dt * 0.01  # cm to m
+
+            if self.state['sub_pos'] is None:
+                self.state['sub_pos'] = eef_xyz_updated[None]
+                # self.state['sub_pos_timestamps'] = command_timestamp
+            else:
+                self.state['sub_pos'] = torch.cat([self.state['sub_pos'], eef_xyz_updated[None]], dim=0)
+                # self.state['sub_pos_timestamps'] = torch.cat([self.state['sub_pos_timestamps'], command_timestamp], dim=0)
+        
+            # if self.state['sub_pos'] is None:
+            #     eef_xyz = self.state['prev_key_pos']
+            # else:
+            #     eef_xyz = self.state['sub_pos'][-1]  # (1, 3), model frame
+            # if self.verbose:
+            #     print(eef_xyz.cpu().numpy().tolist(), end=' ')
+
+            self.step()
+            rendervar, rendervar_full = self.update_rendervar(self.rendervar)
+            self.rendervar = rendervar
+            im, depth = self.render(rendervar_full, 0, bg=[0.0, 0.0, 0.0])
+            im_show = (im.permute(1, 2, 0) * 255.0).cpu().numpy().astype(np.uint8).copy()
+
+            # im_list.append(im_show)
+            cv2.imwrite(str(root / f'log/temp/{i:04}.png'), cv2.cvtColor(im_show, cv2.COLOR_RGB2BGR))
+
+        # self.state['prev_key_pos_timestamp'] = self.state['prev_key_pos_timestamp'] + 20 * dt
+        self.state['v'] *= 0.0
+        self.state['x'] = self.state['x_pred'].clone()
+        self.state['x_his'] = self.state['x'][None].repeat(self.cfg.sim.n_history, 1, 1)
+        self.state['v_his'] *= 0.0
+        self.state['v_pred'] *= 0.0
+
+        make_video(root / 'log/temp', root / f'log/gs/temp/form_video.mp4', '%04d.png', 5)
+
+        form_video = gr.Video(
+            label='Predicted video',
+            value=root / f'log/gs/temp/form_video.mp4',
+            format='mp4',
+            width=self.width,
+            height=self.height,
+        )
+
+        im, depth = self.render(rendervar_full, 0, bg=[0.0, 0.0, 0.0])
+        im_show = (im.permute(1, 2, 0) * 255.0).cpu().numpy().astype(np.uint8).copy()
+
+        gs_pred = save_to_splat(
+            rendervar_full['means3D'].cpu().numpy(),
+            rendervar_full['colors_precomp'].cpu().numpy(),
+            rendervar_full['scales'].cpu().numpy(),
+            rendervar_full['rotations'].cpu().numpy(),
+            rendervar_full['opacities'].cpu().numpy(),
+            root / 'log/gs/temp/gs_pred.splat',
+            rot_rev=True,
+        )
+        form_3dgs_pred = gr.Model3D(
+            label='Predicted Gaussian Splats',
+            height=self.height,
+            value=root / 'log/gs/temp/gs_pred.splat',
+            clear_color=[0, 0, 0, 0],
+        )
+        return form_video, form_3dgs_pred
+    
+    def on_click_run_xplus(self):
+        return self.run_command([5.0, 0, 0])
+
+    def on_click_run_xminus(self):
+        return self.run_command([-5.0, 0, 0])
+    
+    def on_click_run_yplus(self):
+        return self.run_command([0, 5.0, 0])
+    
+    def on_click_run_yminus(self):
+        return self.run_command([0, -5.0, 0])
+    
+    def on_click_run_zplus(self):
+        return self.run_command([0, 0, 5.0])
+    
+    def on_click_run_zminus(self):
+        return self.run_command([0, 0, -5.0])
+    
+    def rotate(self, params, rot_mat):
+        scale = np.linalg.norm(rot_mat, axis=1, keepdims=True)
+        
+        params = {
+            'means3D': pts,
+            'rgb_colors': params['rgb_colors'],
+            'log_scales': params['log_scales'],
+            'unnorm_rotations': quats,
+            'logit_opacities': params['logit_opacities'],
+        }
+        return params
+
+    def launch(self, share=False):
+
+        in_dir = root / 'log/gs/ckpts/rope_scene_1'
+        batch_size = 1
+        num_steps = 1
+        num_particles = self.cfg.sim.n_particles
+        self.load_scaniverse(in_dir)
+        self.init_model(batch_size, num_steps, num_particles, ckpt_path=None)
+
+        params = self.preprocess_gs(self.params)
+        if self.with_bg:
+            self.preprocess_bg_gs()
+        rendervar = self.reset_state(params, visualize_image=False, init=True)
+        rendervar, rendervar_full = self.update_rendervar(rendervar)
+        self.rendervar = rendervar
+
+        im, depth = self.render(rendervar_full, 0, bg=[0.0, 0.0, 0.0])
+        im_show = (im.permute(1, 2, 0) * 255.0).cpu().numpy().astype(np.uint8).copy()
+
+        cv2.imwrite(str(root / 'log/temp_init/0000.png'), cv2.cvtColor(im_show, cv2.COLOR_RGB2BGR))
+
+        make_video(root / 'log/temp_init', root / f'log/gs/temp/form_video_init.mp4', '%04d.png', 1)
+
+        gs_pred = save_to_splat(
+            rendervar_full['means3D'].cpu().numpy(),
+            rendervar_full['colors_precomp'].cpu().numpy(),
+            rendervar_full['scales'].cpu().numpy(),
+            rendervar_full['rotations'].cpu().numpy(),
+            rendervar_full['opacities'].cpu().numpy(),
+            root / 'log/gs/temp/gs_pred.splat',
+            rot_rev=True,
+        )
+
+        with gr.Blocks() as app:
+
+            with gr.Row():
+                gr.Markdown("# Particle-Grid Neural Dynamics for Learning Deformable Object Models from RGB-D Videos")
+
+            with gr.Row():
+                gr.Markdown('### Project page: [https://kywind.github.io/pgnd](https://kywind.github.io/pgnd)')
 
             with gr.Row():
-                width = gr.Slider(
-                    label="Width",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
-
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
+                
+                # with gr.Column(scale=2):
+                #     form_3dgs_orig = gr.Model3D(
+                #         label='Original Gaussian Splats',
+                #         value=None,
+                #     )
+                
+                with gr.Column(scale=2):
+                    form_video = gr.Video(
+                        label='Predicted video',
+                        value=root / f'log/gs/temp/form_video_init.mp4',
+                        format='mp4',
+                        width=self.width,
+                        height=self.height,
+                    )
+                
+                with gr.Column(scale=2):
+                    form_3dgs_pred = gr.Model3D(
+                        label='Predicted Gaussians',
+                        height=self.height,
+                        value=root / 'log/gs/temp/gs_pred.splat',
+                        clear_color=[0, 0, 0, 0],
+                    )
 
+            # Layout
             with gr.Row():
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.0,
-                    maximum=10.0,
-                    step=0.1,
-                    value=0.0,  # Replace with defaults that work for your model
-                )
-
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=50,
-                    step=1,
-                    value=2,  # Replace with defaults that work for your model
-                )
-
-        gr.Examples(examples=examples, inputs=[prompt])
-    gr.on(
-        triggers=[run_button.click, prompt.submit],
-        fn=infer,
-        inputs=[
-            prompt,
-            negative_prompt,
-            seed,
-            randomize_seed,
-            width,
-            height,
-            guidance_scale,
-            num_inference_steps,
-        ],
-        outputs=[result, seed],
-    )
-
-if __name__ == "__main__":
-    demo.launch()
+                with gr.Column(scale=2):
+                    with gr.Row():
+                        run_reset = gr.Button("Reset")
+
+                    with gr.Row():
+                        with gr.Column():
+                            run_xminus = gr.Button("x-")
+                        with gr.Column():
+                            run_xplus = gr.Button("x+")
+
+                    with gr.Row():
+                        with gr.Column():
+                            run_yminus = gr.Button("y-")
+                        with gr.Column():
+                            run_yplus = gr.Button("y+")
+
+                    with gr.Row():
+                        with gr.Column():
+                            run_zminus = gr.Button("z-")
+                        with gr.Column():
+                            run_zplus = gr.Button("z+")
+                
+                with gr.Column(scale=2):
+                    _ = gr.Button(visible=False)  # empty placeholder
+
+            # Set up callbacks
+            run_reset.click(self.reset,
+                    inputs=[],
+                    outputs=[form_video, form_3dgs_pred])
+            
+            run_xplus.click(self.on_click_run_xplus,
+                    inputs=[],
+                    outputs=[form_video, form_3dgs_pred])
+            
+            run_xminus.click(self.on_click_run_xminus,
+                    inputs=[],
+                    outputs=[form_video, form_3dgs_pred])
+            
+            run_yplus.click(self.on_click_run_yplus,
+                    inputs=[],
+                    outputs=[form_video, form_3dgs_pred])
+            
+            run_yminus.click(self.on_click_run_yminus,
+                    inputs=[],
+                    outputs=[form_video, form_3dgs_pred])
+            
+            run_zplus.click(self.on_click_run_zplus,
+                    inputs=[],
+                    outputs=[form_video, form_3dgs_pred])
+
+            run_zminus.click(self.on_click_run_zminus,
+                    inputs=[],
+                    outputs=[form_video, form_3dgs_pred])
+
+        app.launch(share=share)
+
+
+if __name__ == '__main__':
+    visualizer = DynamicsVisualizer()
+    visualizer.launch(share=True)
diff --git a/gradio_utils.py b/gradio_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..716d4fef12acd767c5e19003de87bc5d6683ecf2
--- /dev/null
+++ b/gradio_utils.py
@@ -0,0 +1,155 @@
+import gradio as gr
+import numpy as np
+from PIL import Image, ImageDraw
+
+
+def get_valid_mask(mask: np.ndarray):
+    """Convert mask from gr.Image(0 to 255, RGBA) to binary mask.
+    """
+    if mask.ndim == 3:
+        mask_pil = Image.fromarray(mask).convert('L')
+        mask = np.array(mask_pil)
+    if mask.max() == 255:
+        mask = mask / 255
+    return mask
+
+
+def draw_points_on_image(image, points, intr, extr, z, radius_scale=0.006):
+    overlay_rgba = Image.new("RGBA", image.size, 0)
+    overlay_draw = ImageDraw.Draw(overlay_rgba)
+    for point_key, point in points.items():
+
+        t_color = (255, 100, 100)
+        o_color = (255, 50, 50)
+
+        rad_draw = int(image.size[0] * radius_scale) + 2
+
+        p_start = point["start"]
+        p_target = point["target"]
+
+        if p_start is not None and p_target is not None:
+            p_draw = int(p_start[0]), int(p_start[1])
+            t_draw = int(p_target[0]), int(p_target[1])
+
+            pt = (p_target[0] - p_start[0], p_target[1] - p_start[1])
+            pt_norm = np.linalg.norm(pt)
+            pt_unit = (pt[0] / pt_norm, pt[1] / pt_norm)
+            pt_tang = (pt_unit[1], -pt_unit[0])
+            tt1 = (t_draw[0] + pt_tang[0] * 0.1 * pt_norm - pt_unit[0] * 0.1 * pt_norm,
+                   t_draw[1] + pt_tang[1] * 0.1 * pt_norm - pt_unit[1] * 0.1 * pt_norm)
+            tt2 = (t_draw[0] - pt_tang[0] * 0.1 * pt_norm - pt_unit[0] * 0.1 * pt_norm,
+                   t_draw[1] - pt_tang[1] * 0.1 * pt_norm - pt_unit[1] * 0.1 * pt_norm)
+            tt1_draw = int(tt1[0]), int(tt1[1])
+            tt2_draw = int(tt2[0]), int(tt2[1])
+
+            overlay_draw.line(
+                (p_draw[0], p_draw[1], t_draw[0], t_draw[1]),
+                fill=o_color,
+                width=4,
+            )
+            
+            overlay_draw.line(
+                (t_draw[0], t_draw[1], tt1_draw[0], tt1_draw[1]),
+                fill=o_color,
+                width=4,
+            )
+
+            overlay_draw.line(
+                (t_draw[0], t_draw[1], tt2_draw[0], tt2_draw[1]),
+                fill=o_color,
+                width=4,
+            )
+
+        if p_start is not None:
+            p_draw = int(p_start[0]), int(p_start[1])
+            overlay_draw.ellipse(
+                (
+                    p_draw[0] - rad_draw,
+                    p_draw[1] - rad_draw,
+                    p_draw[0] + rad_draw,
+                    p_draw[1] + rad_draw,
+                ),
+                fill=t_color,
+                outline=o_color,
+                width=2,
+            )
+
+        if p_target is not None:
+            assert p_start is not None
+
+    return Image.alpha_composite(image.convert("RGBA"),
+                                 overlay_rgba).convert("RGB")
+
+
+def draw_raw_points_on_image(image,
+                             points,
+                             # curr_point=None,
+                             # highlight_all=True,
+                             radius_scale=0.002):
+    overlay_rgba = Image.new("RGBA", image.size, 0)
+    overlay_draw = ImageDraw.Draw(overlay_rgba)
+    for p in range(points.shape[0]):
+        point = points[p]
+        t_color = (150, 150, 255)
+        o_color = (50, 50, 255)
+
+        rad_draw = int(image.size[0] * radius_scale)
+
+        t_draw = int(point[0]), int(point[1])
+        overlay_draw.ellipse(
+            (
+                t_draw[0] - rad_draw,
+                t_draw[1] - rad_draw,
+                t_draw[0] + rad_draw,
+                t_draw[1] + rad_draw,
+            ),
+            fill=t_color,
+            outline=o_color,
+        )
+    return Image.alpha_composite(image.convert("RGBA"),
+                                 overlay_rgba).convert("RGB")
+
+
+def draw_mask_on_image(image, mask):
+    im_mask = np.uint8(mask * 255)
+    im_mask_rgba = np.concatenate(
+        (
+            np.tile(im_mask[..., None], [1, 1, 3]),
+            45 * np.ones(
+                (im_mask.shape[0], im_mask.shape[1], 1), dtype=np.uint8),
+        ),
+        axis=-1,
+    )
+    im_mask_rgba = Image.fromarray(im_mask_rgba).convert("RGBA")
+
+    return Image.alpha_composite(image.convert("RGBA"),
+                                 im_mask_rgba).convert("RGB")
+
+
+def on_change_single_global_state(keys,
+                                  value,
+                                  global_state,
+                                  map_transform=None):
+    if map_transform is not None:
+        value = map_transform(value)
+
+    curr_state = global_state
+    if isinstance(keys, str):
+        last_key = keys
+
+    else:
+        for k in keys[:-1]:
+            curr_state = curr_state[k]
+
+        last_key = keys[-1]
+
+    curr_state[last_key] = value
+    return global_state
+
+
+def get_latest_points_pair(points_dict):
+    if not points_dict:
+        return None
+    point_idx = list(points_dict.keys())
+    latest_point_idx = max(point_idx)
+    return latest_point_idx
diff --git a/requirements.txt b/requirements.txt
index 73d01db64bd054c7d21fd0bd79b3af087c468809..82c9eb196dae5fed9a18c1afd52eb747bbd507e5 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,6 +1,25 @@
-accelerate
-diffusers
-invisible_watermark
-torch
+warp-lang==1.1.0
+trimesh
+pyyaml
+numpy==1.26.4
+scipy
 transformers
-xformers
\ No newline at end of file
+scikit-learn
+scikit-image
+pyvista
+opencv-contrib-python
+open3d
+moviepy
+matplotlib
+kornia
+ipdb
+h5py
+gradio
+ffmpeg
+einops
+dill
+av
+timm
+omegaconf
+hydra-core
+wandb
\ No newline at end of file
diff --git a/src/.gitignore b/src/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..8ff95f0c86aab717dbbcc62d313b352cb148dd7c
--- /dev/null
+++ b/src/.gitignore
@@ -0,0 +1,213 @@
+## Mac OS
+
+# General
+.DS_Store
+.AppleDouble
+.LSOverride
+
+# Icon must end with two \r
+Icon
+
+# Thumbnails
+._*
+
+# Files that might appear in the root of a volume
+.DocumentRevisions-V100
+.fseventsd
+.Spotlight-V100
+.TemporaryItems
+.Trashes
+.VolumeIcon.icns
+.com.apple.timemachine.donotpresent
+
+# Directories potentially created on remote AFP share
+.AppleDB
+.AppleDesktop
+Network Trash Folder
+Temporary Items
+.apdisk
+
+## Vim
+
+# Swap
+[._]*.s[a-v][a-z]
+!*.svg  # comment out if you don't need vector files
+[._]*.sw[a-p]
+[._]s[a-rt-v][a-z]
+[._]ss[a-gi-z]
+[._]sw[a-p]
+
+# Session
+Session.vim
+Sessionx.vim
+
+# Temporary
+.netrwhist
+*~
+# Auto-generated tag files
+tags
+# Persistent undo
+[._]*.un~
+
+## Linux
+
+*~
+
+# temporary files which can be created if a process still has a handle open of a deleted file
+.fuse_hidden*
+
+# KDE directory preferences
+.directory
+
+# Linux trash folder which might appear on any partition or disk
+.Trash-*
+
+# .nfs files are created when an open file is removed but is still being accessed
+.nfs*
+
+## Python
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+# env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+## VS Code
+/.vscode
+
+## wandb
+/wandb
+
+## gradio
+.gradio
+
+# log/
+# weights/
+
+/third-party/*
+!/third-party/diff-gaussian-rasterization-w-depth
diff --git a/src/LICENSE b/src/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..9192ce6451fe083011d981c2a24ae02ce1095602
--- /dev/null
+++ b/src/LICENSE
@@ -0,0 +1,7 @@
+Copyright (c) 2011-2024 GitHub Inc.
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
\ No newline at end of file
diff --git a/src/experiments/.root b/src/experiments/.root
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/src/experiments/assets/robots/xarm7/meshes/collision/link1.obj b/src/experiments/assets/robots/xarm7/meshes/collision/link1.obj
new file mode 100644
index 0000000000000000000000000000000000000000..121b252558d16cd6f508e13776f016fca7806fe7
--- /dev/null
+++ b/src/experiments/assets/robots/xarm7/meshes/collision/link1.obj
@@ -0,0 +1,3559 @@
+# https://github.com/mikedh/trimesh
+
+o geometry_0
+v -0.04790971 -0.00026275 -0.09099868 0.54509804 0.71372549 0.60000000
+v -0.04779786 -0.00303987 -0.06612349 0.54509804 0.71372549 0.60000000
+v -0.04786310 0.00166793 -0.09099868 0.54509804 0.71372549 0.60000000
+v -0.04777922 0.00252389 -0.11199402 0.54509804 0.71372549 0.60000000
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diff --git a/src/experiments/assets/robots/xarm7/meshes/collision/link1.obj.convex.stl b/src/experiments/assets/robots/xarm7/meshes/collision/link1.obj.convex.stl
new file mode 100644
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diff --git a/src/experiments/assets/robots/xarm7/meshes/collision/link2.obj b/src/experiments/assets/robots/xarm7/meshes/collision/link2.obj
new file mode 100644
index 0000000000000000000000000000000000000000..b68bc4bcc83a2dba1923ea4e047285ae2c459c7f
--- /dev/null
+++ b/src/experiments/assets/robots/xarm7/meshes/collision/link2.obj
@@ -0,0 +1,5064 @@
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diff --git a/src/experiments/assets/robots/xarm7/meshes/collision/link2.obj.convex.stl b/src/experiments/assets/robots/xarm7/meshes/collision/link2.obj.convex.stl
new file mode 100644
index 0000000000000000000000000000000000000000..5f1875471f1914dec86dbf26e6c97546072ad9bf
--- /dev/null
+++ b/src/experiments/assets/robots/xarm7/meshes/collision/link2.obj.convex.stl
@@ -0,0 +1,2114 @@
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diff --git a/src/experiments/assets/robots/xarm7/meshes/collision/link3.obj b/src/experiments/assets/robots/xarm7/meshes/collision/link3.obj
new file mode 100644
index 0000000000000000000000000000000000000000..b3a9fdd71ee342197ca66740d3221d1cb73170e4
--- /dev/null
+++ b/src/experiments/assets/robots/xarm7/meshes/collision/link3.obj
@@ -0,0 +1,4602 @@
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new file mode 100644
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diff --git a/src/experiments/assets/robots/xarm7/meshes/collision/link4.obj b/src/experiments/assets/robots/xarm7/meshes/collision/link4.obj
new file mode 100644
index 0000000000000000000000000000000000000000..adb7cd707c2057837ced99baee3d364fea01c039
--- /dev/null
+++ b/src/experiments/assets/robots/xarm7/meshes/collision/link4.obj
@@ -0,0 +1,3159 @@
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diff --git a/src/experiments/assets/robots/xarm7/meshes/collision/link5.obj b/src/experiments/assets/robots/xarm7/meshes/collision/link5.obj
new file mode 100644
index 0000000000000000000000000000000000000000..e85144488f5e3617cdfd4c972195d14570733d63
--- /dev/null
+++ b/src/experiments/assets/robots/xarm7/meshes/collision/link5.obj
@@ -0,0 +1,3562 @@
+# https://github.com/mikedh/trimesh
+
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diff --git a/src/experiments/assets/robots/xarm7/meshes/collision/link6.obj b/src/experiments/assets/robots/xarm7/meshes/collision/link6.obj
new file mode 100644
index 0000000000000000000000000000000000000000..b79add388b7063f52c4381f371549f186954d701
--- /dev/null
+++ b/src/experiments/assets/robots/xarm7/meshes/collision/link6.obj
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new file mode 100644
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diff --git a/src/experiments/assets/robots/xarm7/meshes/collision/link7.obj b/src/experiments/assets/robots/xarm7/meshes/collision/link7.obj
new file mode 100644
index 0000000000000000000000000000000000000000..75e049243a4d0208a10c4f202b484748fbd3c84d
--- /dev/null
+++ b/src/experiments/assets/robots/xarm7/meshes/collision/link7.obj
@@ -0,0 +1,573 @@
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diff --git a/src/experiments/assets/robots/xarm7/meshes/collision/link_base.obj b/src/experiments/assets/robots/xarm7/meshes/collision/link_base.obj
new file mode 100644
index 0000000000000000000000000000000000000000..1b5819f498e1e2bf38d965ba68ee1a047ebd22a5
--- /dev/null
+++ b/src/experiments/assets/robots/xarm7/meshes/collision/link_base.obj
@@ -0,0 +1,2472 @@
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+size 381572
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index 0000000000000000000000000000000000000000..863e1932adfb560cf0720f904cbb8e692ec49f88
--- /dev/null
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@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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index 0000000000000000000000000000000000000000..60aa2e58f5a70692065f285ee2461a241f738bb9
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@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 621744
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new file mode 100644
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--- /dev/null
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@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 336888
diff --git a/src/experiments/assets/robots/xarm7/meshes/visual/link_base.glb b/src/experiments/assets/robots/xarm7/meshes/visual/link_base.glb
new file mode 100644
index 0000000000000000000000000000000000000000..8c85e679a56bc7621662fe017c8948bcaf8358b0
--- /dev/null
+++ b/src/experiments/assets/robots/xarm7/meshes/visual/link_base.glb
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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diff --git a/src/experiments/assets/robots/xarm7/xarm7.urdf b/src/experiments/assets/robots/xarm7/xarm7.urdf
new file mode 100644
index 0000000000000000000000000000000000000000..659aa8d217591effde4b0dd17a7c6d6ff66ac4c4
--- /dev/null
+++ b/src/experiments/assets/robots/xarm7/xarm7.urdf
@@ -0,0 +1,219 @@
+<?xml version="1.0" encoding="utf-8"?>
+<!--GLB Source: https://github.com/sapien-sim/xarm7/tree/master/sapien_xarm7/xarm_urdf/xarm_description/meshes-->
+<robot name="xarm7">
+  <link name="link_base">
+    <inertial>
+      <origin rpy="0 0 0" xyz="-0.021131 -0.0016302 0.056488"/>
+      <mass value="0.88556"/>
+      <inertia ixx="0.0030595" ixy="0.00012259" ixz="-0.00062705" iyy="0.0037783" iyz="0.00027023" izz="0.0020125"/>
+    </inertial>
+    <visual>
+      <geometry>
+        <mesh filename="meshes/visual/link_base.glb"/>
+      </geometry>
+      <origin rpy="0 0 0" xyz="0 0 0"/>
+    </visual>
+    <collision>
+      <geometry>
+        <mesh filename="meshes/collision/link_base.obj"/>
+      </geometry>
+      <origin rpy="0 0 0" xyz="0 0 0"/>
+    </collision>
+  </link>
+  <link name="link1">
+    <inertial>
+      <origin rpy="0 0 0" xyz="-0.0002 0.02905 -0.01233"/>
+      <mass value="2.382"/>
+      <inertia ixx="0.0056905" ixy="-1.579e-05" ixz="5.125e-06" iyy="0.0049566" iyz="-0.000873378" izz="0.003316654"/>
+    </inertial>
+    <visual>
+      <geometry>
+        <mesh filename="meshes/visual/link1.glb"/>
+      </geometry>
+      <origin rpy="0 0 0" xyz="0 0 0"/>
+    </visual>
+    <collision>
+      <geometry>
+        <mesh filename="meshes/collision/link1.obj"/>
+      </geometry>
+      <origin rpy="0 0 0" xyz="0 0 0"/>
+    </collision>
+  </link>
+  <joint name="joint1" type="revolute">
+    <origin rpy="0 0 0" xyz="0 0 0.267"/>
+    <parent link="link_base"/>
+    <child link="link1"/>
+    <axis xyz="0 0 1"/>
+    <limit effort="50" lower="-6.28318530718" upper="6.28318530718" velocity="3.14"/>
+    <dynamics damping="10" friction="1"/>
+  </joint>
+  <link name="link2">
+    <inertial>
+      <origin rpy="0 0 0" xyz="0.00022 -0.12856 0.01735"/>
+      <mass value="1.869"/>
+      <inertia ixx="0.0095989" ixy="1.541e-06" ixz="5.56e-06" iyy="0.00382472" iyz="-0.00317156" izz="0.007565669"/>
+    </inertial>
+    <visual>
+      <geometry>
+        <mesh filename="meshes/visual/link2.glb"/>
+      </geometry>
+      <origin rpy="0 0 0" xyz="0 0 0"/>
+    </visual>
+    <collision>
+      <geometry>
+        <mesh filename="meshes/collision/link2.obj"/>
+      </geometry>
+      <origin rpy="0 0 0" xyz="0 0 0"/>
+    </collision>
+  </link>
+  <joint name="joint2" type="revolute">
+    <origin rpy="-1.5708 0 0" xyz="0 0 0"/>
+    <parent link="link1"/>
+    <child link="link2"/>
+    <axis xyz="0 0 1"/>
+    <limit effort="50" lower="-2.059" upper="2.0944" velocity="3.14"/>
+    <dynamics damping="10" friction="1"/>
+  </joint>
+  <link name="link3">
+    <inertial>
+      <origin rpy="0 0 0" xyz="0.0466 -0.02463 -0.00768"/>
+      <mass value="1.6383"/>
+      <inertia ixx="0.00310955" ixy="0.00030837" ixz="-0.00058453" iyy="0.00264483" iyz="0.000338893" izz="0.0026624"/>
+    </inertial>
+    <visual>
+      <geometry>
+        <mesh filename="meshes/visual/link3.glb"/>
+      </geometry>
+      <origin rpy="0 0 0" xyz="0 0 0"/>
+    </visual>
+    <collision>
+      <geometry>
+        <mesh filename="meshes/collision/link3.obj"/>
+      </geometry>
+      <origin rpy="0 0 0" xyz="0 0 0"/>
+    </collision>
+  </link>
+  <joint name="joint3" type="revolute">
+    <origin rpy="1.5708 0 0" xyz="0 -0.293 0"/>
+    <parent link="link2"/>
+    <child link="link3"/>
+    <axis xyz="0 0 1"/>
+    <limit effort="30" lower="-6.28318530718" upper="6.28318530718" velocity="3.14"/>
+    <dynamics damping="5" friction="1"/>
+  </joint>
+  <link name="link4">
+    <inertial>
+      <origin rpy="0 0 0" xyz="0.07047 -0.11575 0.012"/>
+      <mass value="1.7269"/>
+      <inertia ixx="0.005889" ixy="0.00137112" ixz="0.00088143" iyy="0.00359703" iyz="-0.001762155" izz="0.00543244"/>
+    </inertial>
+    <visual>
+      <geometry>
+        <mesh filename="meshes/visual/link4.glb"/>
+      </geometry>
+      <origin rpy="0 0 0" xyz="0 0 0"/>
+    </visual>
+    <collision>
+      <geometry>
+        <mesh filename="meshes/collision/link4.obj"/>
+      </geometry>
+      <origin rpy="0 0 0" xyz="0 0 0"/>
+    </collision>
+  </link>
+  <joint name="joint4" type="revolute">
+    <origin rpy="1.5708 0 0" xyz="0.0525 0 0"/>
+    <parent link="link3"/>
+    <child link="link4"/>
+    <axis xyz="0 0 1"/>
+    <limit effort="30" lower="-0.19198" upper="3.927" velocity="3.14"/>
+    <dynamics damping="5" friction="1"/>
+  </joint>
+  <link name="link5">
+    <inertial>
+      <origin rpy="0 0 0" xyz="-0.00032 0.01604 -0.026"/>
+      <mass value="1.3203"/>
+      <inertia ixx="0.00534665" ixy="1.5117e-05" ixz="-3.69e-07" iyy="0.0049779" iyz="-0.00022132" izz="0.0013624"/>
+    </inertial>
+    <visual>
+      <geometry>
+        <mesh filename="meshes/visual/link5.glb"/>
+      </geometry>
+      <origin rpy="0 0 0" xyz="0 0 0"/>
+    </visual>
+    <collision>
+      <geometry>
+        <mesh filename="meshes/collision/link5.obj"/>
+      </geometry>
+      <origin rpy="0 0 0" xyz="0 0 0"/>
+    </collision>
+  </link>
+  <joint name="joint5" type="revolute">
+    <origin rpy="1.5708 0 0" xyz="0.0775 -0.3425 0"/>
+    <parent link="link4"/>
+    <child link="link5"/>
+    <axis xyz="0 0 1"/>
+    <limit effort="30" lower="-6.28318530718" upper="6.28318530718" velocity="3.14"/>
+    <dynamics damping="5" friction="1"/>
+  </joint>
+  <link name="link6">
+    <inertial>
+      <origin rpy="0 0 0" xyz="0.06469 0.03278 0.02141"/>
+      <mass value="1.325"/>
+      <inertia ixx="0.0014745" ixy="-0.000488" ixz="0.0002953" iyy="0.0019037" iyz="0.00014749" izz="0.0023652"/>
+    </inertial>
+    <visual>
+      <geometry>
+        <mesh filename="meshes/visual/link6.glb"/>
+      </geometry>
+      <origin rpy="0 0 0" xyz="0 0 0"/>
+    </visual>
+    <collision>
+      <geometry>
+        <mesh filename="meshes/collision/link6.obj"/>
+      </geometry>
+      <origin rpy="0 0 0" xyz="0 0 0"/>
+    </collision>
+  </link>
+  <joint name="joint6" type="revolute">
+    <origin rpy="1.5708 0 0" xyz="0 0 0"/>
+    <parent link="link5"/>
+    <child link="link6"/>
+    <axis xyz="0 0 1"/>
+    <limit effort="20" lower="-1.69297" upper="3.14159265359" velocity="3.14"/>
+    <dynamics damping="2" friction="1"/>
+  </joint>
+  <link name="link7">
+    <inertial>
+      <origin rpy="0 0 0" xyz="0 -0.00677 -0.01098"/>
+      <mass value="0.17"/>
+      <inertia ixx="9.3e-05" ixy="-0.0" ixz="-0.0" iyy="5.87e-05" iyz="-3.6e-06" izz="0.000132"/>
+    </inertial>
+    <visual>
+      <geometry>
+        <mesh filename="meshes/visual/link7.glb"/>
+      </geometry>
+      <origin rpy="0 0 0" xyz="0 0 0"/>
+    </visual>
+    <collision>
+      <geometry>
+        <mesh filename="meshes/collision/link7.obj"/>
+      </geometry>
+      <origin rpy="0 0 0" xyz="0 0 0"/>
+    </collision>
+  </link>
+  <joint name="joint7" type="revolute">
+    <origin rpy="-1.5708 0 0" xyz="0.076 0.097 0"/>
+    <parent link="link6"/>
+    <child link="link7"/>
+    <axis xyz="0 0 1"/>
+    <limit effort="20" lower="-6.28318530718" upper="6.28318530718" velocity="3.14"/>
+    <dynamics damping="2" friction="1"/>
+  </joint>
+  <link name="link_eef"/>
+  <joint name="joint_eef" type="fixed">
+    <origin rpy="0 0 0" xyz="0 0 0"/>
+    <parent link="link7"/>
+    <child link="link_eef"/>
+  </joint>
+</robot>
+
diff --git a/src/experiments/cfg/default.yaml b/src/experiments/cfg/default.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..016330101898daba2695f1cd4a77806648492713
--- /dev/null
+++ b/src/experiments/cfg/default.yaml
@@ -0,0 +1,22 @@
+defaults:
+  - model: pgnd
+  - render: default
+  - sim: default
+  - train: default
+  - _self_
+  - override hydra/job_logging: disabled
+  - override hydra/hydra_logging: disabled
+
+hydra:
+  output_subdir: null
+  run:
+    dir: .
+
+seed: 0
+cpu: 0
+num_cpus: 128
+gpus: 
+  - 0
+overwrite: False
+resume: False
+debug: False
diff --git a/src/experiments/cfg/model/friction/default.yaml b/src/experiments/cfg/model/friction/default.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..49d91e76adb91d6d51cf7aa937c7952fceffb112
--- /dev/null
+++ b/src/experiments/cfg/model/friction/default.yaml
@@ -0,0 +1,2 @@
+value: 0.0
+requires_grad: False
\ No newline at end of file
diff --git a/src/experiments/cfg/model/material/pointnet_nerf_material.yaml b/src/experiments/cfg/model/material/pointnet_nerf_material.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..d42466b0f4875e640f765f5f9bcf681ab50441e9
--- /dev/null
+++ b/src/experiments/cfg/model/material/pointnet_nerf_material.yaml
@@ -0,0 +1,6 @@
+requires_grad: True
+output_scale: 1.0
+input_scale: 2.0
+radius: 0.2
+absolute_y: False
+pe_num_func_res: 0
\ No newline at end of file
diff --git a/src/experiments/cfg/model/pgnd.yaml b/src/experiments/cfg/model/pgnd.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..cd72da056c4f152716ccc6f796133b7adc9b4f3e
--- /dev/null
+++ b/src/experiments/cfg/model/pgnd.yaml
@@ -0,0 +1,9 @@
+defaults:
+  - material: pointnet_nerf_material
+  - friction: default
+  - _self_
+
+ckpt: null
+clip_bound: 1.5
+eef_t: [0.0, 0.0, 0.01]
+gripper_radius: 0.04
diff --git a/src/experiments/cfg/render/default.yaml b/src/experiments/cfg/render/default.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..6299b0a04e7d62ad18382242f7f97e62b35ec1ed
--- /dev/null
+++ b/src/experiments/cfg/render/default.yaml
@@ -0,0 +1,11 @@
+width: 512
+height: 512
+skip_frame: 1
+bound: 1.5
+fps: 5
+radius_scale: 500
+center: [0.5, 0.3, 0.5]
+distance: 1.4
+azimuth: -125
+elevation: 30
+reflectance: [0.92941176, 0.32941176, 0.23137255]
diff --git a/src/experiments/cfg/sim/default.yaml b/src/experiments/cfg/sim/default.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..08510cf068e5f7fd9ebddee7d8eeac9cd7e7c56c
--- /dev/null
+++ b/src/experiments/cfg/sim/default.yaml
@@ -0,0 +1,16 @@
+num_steps_train: 5
+num_steps: 1000
+interval: 1
+num_grids: [50, 50, 50, 0.02]
+dt: 0.1
+bound: 3
+eps: 1e-7
+skip_frame: 1
+num_grippers: 1
+preprocess_scale: 1.0
+preprocess_with_table: True
+n_particles: 1000
+gripper_forcing: True
+gripper_points: False
+n_history: 2
+uniform: False
diff --git a/src/experiments/cfg/train/default.yaml b/src/experiments/cfg/train/default.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..9e171e82792540c0f949a1b52d877d10d5926fe7
--- /dev/null
+++ b/src/experiments/cfg/train/default.yaml
@@ -0,0 +1,30 @@
+name: null
+dataset_name: null
+source_dataset_name: null
+
+num_iterations: 100000
+resume_iteration: 0
+batch_size: 32
+num_workers: 8
+material_lr: 1e-4
+material_wd: 0.0
+material_grad_max_norm: 0.1
+training_start_episode: 0
+training_end_episode: 1000
+eval_start_episode: 0
+eval_end_episode: 10
+iteration_log_interval: 10
+iteration_save_interval: 1000
+iteration_eval_interval: 10000
+loss_factor: 1.0
+loss_factor_v: 0.0
+loss_factor_x: 100.0
+friction_lr: 1e-1
+friction_wd: 0.0
+friction_grad_max_norm: 0.1
+dataset_load_skip_frame: 1
+dataset_skip_frame: 1
+downsample: False
+use_pv: False
+use_gs: False
+dataset_non_overwrite: True
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new file mode 100644
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--- /dev/null
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diff --git a/src/experiments/log/gs/ckpts/rope_scene_1/eef_xyz.txt b/src/experiments/log/gs/ckpts/rope_scene_1/eef_xyz.txt
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index 0000000000000000000000000000000000000000..4da5e2fc9745916aad85958f3e4da0f5725217a2
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--- /dev/null
+++ b/src/experiments/log/rope/train/ckpt/100000.pt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:eaa05e84337cd94869d6359b6589e4bf7a0c0d386c85f8bd4fa8aa53d0f76262
+size 3374922
diff --git a/src/experiments/log/rope/train/hydra.yaml b/src/experiments/log/rope/train/hydra.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..3e833a1d92860e321d1b9e54bf8317a010d145d2
--- /dev/null
+++ b/src/experiments/log/rope/train/hydra.yaml
@@ -0,0 +1,95 @@
+model:
+  material:
+    requires_grad: true
+    output_scale: 1.0
+    input_scale: 2.0
+    radius: 0.2
+    absolute_y: false
+    pe_num_func_res: 0
+  friction:
+    value: 0.0
+    requires_grad: false
+  ckpt: null
+  clip_bound: 1.5
+  eef_t:
+  - 0.0
+  - 0.0
+  - 0.01
+  gripper_radius: 0.04
+render:
+  width: 512
+  height: 512
+  skip_frame: 1
+  bound: 1.5
+  fps: 5
+  radius_scale: 500
+  center:
+  - 0.5
+  - 0.3
+  - 0.5
+  distance: 1.4
+  azimuth: -125
+  elevation: 30
+  reflectance:
+  - 0.92941176
+  - 0.32941176
+  - 0.23137255
+sim:
+  num_steps_train: 5
+  num_steps: 1000
+  interval: 1
+  num_grids:
+  - 50
+  - 50
+  - 50
+  - 0.02
+  dt: 0.1
+  bound: 3
+  eps: 1.0e-07
+  skip_frame: 1
+  num_grippers: 1
+  preprocess_scale: 1.0
+  preprocess_with_table: true
+  n_particles: 1000
+  gripper_forcing: true
+  gripper_points: false
+  n_history: 2
+  uniform: false
+train:
+  name: rope/train
+  dataset_name: rope/dataset
+  source_dataset_name: data/rope_merged/sub_episodes_v
+  num_iterations: 100000
+  resume_iteration: 0
+  batch_size: 32
+  num_workers: 8
+  material_lr: 0.0001
+  material_wd: 0.0
+  material_grad_max_norm: 0.1
+  training_start_episode: 0
+  training_end_episode: 651
+  eval_start_episode: 651
+  eval_end_episode: 691
+  iteration_log_interval: 10
+  iteration_save_interval: 1000
+  iteration_eval_interval: 10000
+  loss_factor: 1.0
+  loss_factor_v: 0.0
+  loss_factor_x: 100.0
+  friction_lr: 0.1
+  friction_wd: 0.0
+  friction_grad_max_norm: 0.1
+  dataset_load_skip_frame: 3
+  dataset_skip_frame: 1
+  downsample: false
+  use_pv: false
+  use_gs: false
+  dataset_non_overwrite: true
+seed: 0
+cpu: 0
+num_cpus: 128
+gpus:
+- 0
+overwrite: false
+resume: true
+debug: false
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--- /dev/null
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--- /dev/null
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new file mode 100644
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--- /dev/null
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--- /dev/null
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new file mode 100644
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--- /dev/null
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new file mode 100644
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--- /dev/null
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new file mode 100644
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--- /dev/null
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diff --git a/src/experiments/log/temp_init/0000.png b/src/experiments/log/temp_init/0000.png
new file mode 100644
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--- /dev/null
+++ b/src/experiments/log/temp_init/0000.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 290036
diff --git a/src/experiments/real_world/calibrate.py b/src/experiments/real_world/calibrate.py
new file mode 100644
index 0000000000000000000000000000000000000000..a8fc0b55d5c8c6f26f78ba8b5bfb93925a5f231e
--- /dev/null
+++ b/src/experiments/real_world/calibrate.py
@@ -0,0 +1,127 @@
+import cv2
+import argparse
+import time
+import numpy as np
+import torch
+import open3d as o3d
+from PIL import Image
+
+from modules import RobotEnv, BimanualRobotEnv
+from utils.pcd_utils import get_tabletop_points, visualize_o3d
+
+def construct_goal_from_perception():
+    use_robot = True
+
+    exposure_time = 10
+    env = RobotEnv(
+        WH=[848, 480],
+        obs_fps=5,
+        n_obs_steps=2,
+        use_robot=use_robot,
+        speed=100,
+    )
+
+    try:
+        env.start(exposure_time=exposure_time)
+        if use_robot:
+            env.reset_robot()
+        print('env started')
+        time.sleep(exposure_time)
+        print('start recording')
+
+        env.calibrate(re_calibrate=False)
+
+        obs = env.get_obs(get_color=True, get_depth=True)
+        intr_list = env.get_intrinsics()
+        R_list, t_list = env.get_extrinsics()
+        bbox = env.get_bbox()
+
+        rgb_list = []
+        depth_list = []
+        for i in range(4):
+            rgb = obs[f'color_{i}'][-1]
+            depth = obs[f'depth_{i}'][-1]
+            rgb_list.append(rgb)
+            depth_list.append(depth)
+
+        pcd = get_tabletop_points(rgb_list, depth_list, R_list, t_list, intr_list, bbox)
+
+        visualize_o3d([pcd])
+        o3d.io.write_point_cloud("vis_real_world/target.pcd", pcd)
+
+    finally:
+        env.stop()
+        print('env stopped')
+
+
+def calibrate(use_robot=True, reset_robot=True, wrist=None):
+    exposure_time = 5
+    env = RobotEnv(
+        use_camera=True,
+        WH=[1280, 720],
+        obs_fps=5,
+        n_obs_steps=2,
+        use_robot=use_robot,
+        speed=100,
+        wrist=wrist,
+    )
+
+    try:
+        env.start(exposure_time=exposure_time)
+        if use_robot and reset_robot:
+            env.reset_robot()
+        print('env started')
+        time.sleep(exposure_time)
+        print('start recording')
+
+        env.calibrate(re_calibrate=True)
+    
+    finally:
+        env.stop()
+        print('env stopped')
+
+
+def calibrate_bimanual(reset_robot=True):
+
+    exposure_time = 5
+    env = BimanualRobotEnv(
+        WH=[1280, 720],
+        obs_fps=5,
+        n_obs_steps=2,
+        speed=100,
+        gripper_enable=True,
+    )
+
+    try:
+        env.start(exposure_time=exposure_time)
+        if reset_robot:
+            env.reset_robot()
+        print('env started')
+        time.sleep(exposure_time)
+        print('start recording')
+
+        env.calibrate(re_calibrate=True)
+    
+    finally:
+        env.stop()
+        print('env stopped')
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--calibrate", action="store_true")
+    parser.add_argument("--calibrate_bimanual", action="store_true")
+    parser.add_argument("--calibrate_fixed", action="store_true")
+    parser.add_argument("--construct_goal", action="store_true")
+    parser.add_argument("--examine_points", action="store_true")
+    args = parser.parse_args()
+    if args.calibrate:
+        calibrate(reset_robot=False)
+    elif args.calibrate_bimanual:
+        calibrate_bimanual(reset_robot=False)
+    elif args.calibrate_fixed:  # only calibrate fixed cameras
+        calibrate(use_robot=False)
+    elif args.construct_goal:
+        construct_goal_from_perception()
+    else:
+        print("No arguments provided")
diff --git a/src/experiments/real_world/camera/__init__.py b/src/experiments/real_world/camera/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/src/experiments/real_world/camera/multi_realsense.py b/src/experiments/real_world/camera/multi_realsense.py
new file mode 100644
index 0000000000000000000000000000000000000000..f006b753a00d02207060c0fa7d2f9ba099002e78
--- /dev/null
+++ b/src/experiments/real_world/camera/multi_realsense.py
@@ -0,0 +1,183 @@
+from typing import List, Optional, Union, Dict, Callable
+import numbers
+import time
+from multiprocess.managers import SharedMemoryManager
+import numpy as np
+import pyrealsense2 as rs
+from .single_realsense import SingleRealsense
+
+class MultiRealsense:
+    def __init__(self,
+        serial_numbers: Optional[List[str]]=None,
+        shm_manager: Optional[SharedMemoryManager]=None,
+        resolution=(1280,720),
+        capture_fps=30,
+        put_fps=None,
+        put_downsample=True,
+        enable_color=True,
+        enable_depth=False,
+        process_depth=False,
+        enable_infrared=False,
+        get_max_k=30,
+        advanced_mode_config: Optional[Union[dict, List[dict]]]=None,
+        transform: Optional[Union[Callable[[Dict], Dict], List[Callable]]]=None,
+        vis_transform: Optional[Union[Callable[[Dict], Dict], List[Callable]]]=None,
+        verbose=False
+        ):
+        if shm_manager is None:
+            shm_manager = SharedMemoryManager()
+            shm_manager.start()
+        if serial_numbers is None:
+            serial_numbers = SingleRealsense.get_connected_devices_serial()
+        n_cameras = len(serial_numbers)
+
+        advanced_mode_config = repeat_to_list(
+            advanced_mode_config, n_cameras, dict)
+        transform = repeat_to_list(
+            transform, n_cameras, Callable)
+        vis_transform = repeat_to_list(
+            vis_transform, n_cameras, Callable)
+
+        cameras = dict()
+        for i, serial in enumerate(serial_numbers):
+            cameras[serial] = SingleRealsense(
+                shm_manager=shm_manager,
+                serial_number=serial,
+                resolution=resolution,
+                capture_fps=capture_fps,
+                put_fps=put_fps,
+                put_downsample=put_downsample,
+                enable_color=enable_color,
+                enable_depth=enable_depth,
+                process_depth=process_depth,
+                enable_infrared=enable_infrared,
+                get_max_k=get_max_k,
+                advanced_mode_config=advanced_mode_config[i],
+                transform=transform[i],
+                vis_transform=vis_transform[i],
+                is_master=(i == 0),
+                verbose=verbose
+            )
+        
+        self.cameras = cameras
+        self.serial_numbers = serial_numbers
+        self.shm_manager = shm_manager
+        self.resolution = resolution
+        self.capture_fps = capture_fps
+
+    def __enter__(self):
+        self.start()
+        return self
+    
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        self.stop()
+    
+    @property
+    def n_cameras(self):
+        return len(self.cameras)
+    
+    @property
+    def is_ready(self):
+        is_ready = True
+        for camera in self.cameras.values():
+            if not camera.is_ready:
+                is_ready = False
+        return is_ready
+    
+    def start(self, wait=True, put_start_time=None):
+        if put_start_time is None:
+            put_start_time = time.time()
+        for camera in self.cameras.values():
+            camera.start(wait=False, put_start_time=put_start_time)
+
+        if wait:
+            self.start_wait()
+    
+    def stop(self, wait=True):
+        for camera in self.cameras.values():
+            camera.stop(wait=False)
+        
+        if wait:
+            self.stop_wait()
+
+    def start_wait(self):
+        for camera in self.cameras.values():
+            print('processing camera {}'.format(camera.serial_number))
+            camera.start_wait()
+
+    def stop_wait(self):
+        for camera in self.cameras.values():
+            camera.join()
+    
+    def get(self, k=None, index=None, out=None) -> Dict[int, Dict[str, np.ndarray]]:
+        """
+        Return order T,H,W,C
+        {
+            0: {
+                'rgb': (T,H,W,C),
+                'timestamp': (T,)
+            },
+            1: ...
+        }
+        """
+        if index is not None:
+            this_out = None
+            this_out = self.cameras[self.serial_numbers[index]].get(k=k, out=this_out)
+            return this_out
+        if out is None:
+            out = dict()
+        for i, camera in enumerate(self.cameras.values()):
+            this_out = None
+            if i in out:
+                this_out = out[i]
+            this_out = camera.get(k=k, out=this_out)
+            out[i] = this_out
+        return out
+    
+    def set_color_option(self, option, value):
+        n_camera = len(self.cameras)
+        value = repeat_to_list(value, n_camera, numbers.Number)
+        for i, camera in enumerate(self.cameras.values()):
+            camera.set_color_option(option, value[i])
+
+    def set_exposure(self, exposure=None, gain=None):
+        """150nit. (0.1 ms, 1/10000s)
+        gain: (0, 128)
+        """
+
+        if exposure is None and gain is None:
+            # auto exposure
+            self.set_color_option(rs.option.enable_auto_exposure, 1.0)
+        else:
+            # manual exposure
+            self.set_color_option(rs.option.enable_auto_exposure, 0.0)
+            if exposure is not None:
+                self.set_color_option(rs.option.exposure, exposure)
+            if gain is not None:
+                self.set_color_option(rs.option.gain, gain)
+    
+    def set_white_balance(self, white_balance=None):
+        if white_balance is None:
+            self.set_color_option(rs.option.enable_auto_white_balance, 1.0)
+        else:
+            self.set_color_option(rs.option.enable_auto_white_balance, 0.0)
+            self.set_color_option(rs.option.white_balance, white_balance)
+
+    def get_intrinsics(self):
+        return np.array([c.get_intrinsics() for c in self.cameras.values()])
+
+    def get_depth_scale(self):
+        return np.array([c.get_depth_scale() for c in self.cameras.values()])
+    
+    def restart_put(self, start_time):
+        for camera in self.cameras.values():
+            camera.restart_put(start_time)
+
+
+def repeat_to_list(x, n: int, cls):
+    if x is None:
+        x = [None] * n
+    if isinstance(x, cls):
+        x = [x] * n
+    assert len(x) == n
+    return x
diff --git a/src/experiments/real_world/camera/shared_memory/__init__.py b/src/experiments/real_world/camera/shared_memory/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/src/experiments/real_world/camera/shared_memory/shared_memory_queue.py b/src/experiments/real_world/camera/shared_memory/shared_memory_queue.py
new file mode 100644
index 0000000000000000000000000000000000000000..883a713b1d0157339f50d6330cebfe256a206478
--- /dev/null
+++ b/src/experiments/real_world/camera/shared_memory/shared_memory_queue.py
@@ -0,0 +1,187 @@
+from typing import Dict, List, Union
+import numbers
+from queue import Empty, Full
+from multiprocess.managers import SharedMemoryManager
+import numpy as np
+from .shared_memory_util import ArraySpec, SharedAtomicCounter
+from .shared_ndarray import SharedNDArray
+
+
+class SharedMemoryQueue:
+    """
+    A Lock-Free FIFO Shared Memory Data Structure.
+    Stores a sequence of dict of numpy arrays.
+    """
+
+    def __init__(self,
+            shm_manager: SharedMemoryManager,
+            array_specs: List[ArraySpec],
+            buffer_size: int
+        ):
+
+        # create atomic counter
+        write_counter = SharedAtomicCounter(shm_manager)
+        read_counter = SharedAtomicCounter(shm_manager)
+        
+        # allocate shared memory
+        shared_arrays = dict()
+        for spec in array_specs:
+            key = spec.name
+            assert key not in shared_arrays
+            array = SharedNDArray.create_from_shape(
+                mem_mgr=shm_manager,
+                shape=(buffer_size,) + tuple(spec.shape),
+                dtype=spec.dtype)
+            shared_arrays[key] = array
+        
+        self.buffer_size = buffer_size
+        self.array_specs = array_specs
+        self.write_counter = write_counter
+        self.read_counter = read_counter
+        self.shared_arrays = shared_arrays
+    
+    @classmethod
+    def create_from_examples(cls, 
+            shm_manager: SharedMemoryManager,
+            examples: Dict[str, Union[np.ndarray, numbers.Number]], 
+            buffer_size: int
+            ):
+        specs = list()
+        for key, value in examples.items():
+            shape = None
+            dtype = None
+            if isinstance(value, np.ndarray):
+                shape = value.shape
+                dtype = value.dtype
+                assert dtype != np.dtype('O')
+            elif isinstance(value, numbers.Number):
+                shape = tuple()
+                dtype = np.dtype(type(value))
+            else:
+                raise TypeError(f'Unsupported type {type(value)}')
+
+            spec = ArraySpec(
+                name=key,
+                shape=shape,
+                dtype=dtype
+            )
+            specs.append(spec)
+
+        obj = cls(
+            shm_manager=shm_manager,
+            array_specs=specs,
+            buffer_size=buffer_size
+            )
+        return obj
+    
+    def qsize(self):
+        read_count = self.read_counter.load()
+        write_count = self.write_counter.load()
+        n_data = write_count - read_count
+        return n_data
+    
+    def empty(self):
+        n_data = self.qsize()
+        return n_data <= 0
+    
+    def clear(self):
+        self.read_counter.store(self.write_counter.load())
+    
+    def put(self, data: Dict[str, Union[np.ndarray, numbers.Number]]):
+        read_count = self.read_counter.load()
+        write_count = self.write_counter.load()
+        n_data = write_count - read_count
+        if n_data >= self.buffer_size:
+            raise Full()
+        
+        next_idx = write_count % self.buffer_size
+
+        # write to shared memory
+        for key, value in data.items():
+            arr: np.ndarray
+            arr = self.shared_arrays[key].get()
+            if isinstance(value, np.ndarray):
+                arr[next_idx] = value
+            else:
+                arr[next_idx] = np.array(value, dtype=arr.dtype)
+
+        # update idx
+        self.write_counter.add(1)
+    
+    def get(self, out=None) -> Dict[str, np.ndarray]:
+        write_count = self.write_counter.load()
+        read_count = self.read_counter.load()
+        n_data = write_count - read_count
+        if n_data <= 0:
+            raise Empty()
+
+        if out is None:
+            out = self._allocate_empty()
+
+        next_idx = read_count % self.buffer_size
+        for key, value in self.shared_arrays.items():
+            arr = value.get()
+            np.copyto(out[key], arr[next_idx])
+        
+        # update idx
+        self.read_counter.add(1)
+        return out
+
+    def get_k(self, k, out=None) -> Dict[str, np.ndarray]:
+        write_count = self.write_counter.load()
+        read_count = self.read_counter.load()
+        n_data = write_count - read_count
+        if n_data <= 0:
+            raise Empty()
+        assert k <= n_data
+
+        out = self._get_k_impl(k, read_count, out=out)
+        self.read_counter.add(k)
+        return out
+
+    def get_all(self, out=None) -> Dict[str, np.ndarray]:
+        write_count = self.write_counter.load()
+        read_count = self.read_counter.load()
+        n_data = write_count - read_count
+        if n_data <= 0:
+            raise Empty()
+
+        out = self._get_k_impl(n_data, read_count, out=out)
+        self.read_counter.add(n_data)
+        return out
+    
+    def _get_k_impl(self, k, read_count, out=None) -> Dict[str, np.ndarray]:
+        if out is None:
+            out = self._allocate_empty(k)
+
+        curr_idx = read_count % self.buffer_size
+        for key, value in self.shared_arrays.items():
+            arr = value.get()
+            target = out[key]
+
+            start = curr_idx
+            end = min(start + k, self.buffer_size)
+            target_start = 0
+            target_end = (end - start)
+            target[target_start: target_end] = arr[start:end]
+
+            remainder = k - (end - start)
+            if remainder > 0:
+                # wrap around
+                start = 0
+                end = start + remainder
+                target_start = target_end
+                target_end = k
+                target[target_start: target_end] = arr[start:end]
+
+        return out
+    
+    def _allocate_empty(self, k=None):
+        result = dict()
+        for spec in self.array_specs:
+            shape = spec.shape
+            if k is not None:
+                shape = (k,) + shape
+            result[spec.name] = np.empty(
+                shape=shape, dtype=spec.dtype)
+        return result
diff --git a/src/experiments/real_world/camera/shared_memory/shared_memory_ring_buffer.py b/src/experiments/real_world/camera/shared_memory/shared_memory_ring_buffer.py
new file mode 100644
index 0000000000000000000000000000000000000000..d23303078692b29858ee6eba19d5c0e6073ce292
--- /dev/null
+++ b/src/experiments/real_world/camera/shared_memory/shared_memory_ring_buffer.py
@@ -0,0 +1,221 @@
+from typing import Dict, List, Union
+
+from queue import Empty
+import numbers
+import time
+from multiprocess.managers import SharedMemoryManager
+import numpy as np
+
+from .shared_ndarray import SharedNDArray
+from .shared_memory_util import ArraySpec, SharedAtomicCounter
+
+class SharedMemoryRingBuffer:
+    """
+    A Lock-Free FILO Shared Memory Data Structure.
+    Stores a sequence of dict of numpy arrays.
+    """
+
+    def __init__(self, 
+            shm_manager: SharedMemoryManager,
+            array_specs: List[ArraySpec],
+            get_max_k: int,
+            get_time_budget: float,
+            put_desired_frequency: float,
+            safety_margin: float=1.5
+        ):
+        """
+        shm_manager: Manages the life cycle of share memories 
+            across processes. Remember to run .start() before passing.
+        array_specs: Name, shape and type of arrays for a single time step.
+        get_max_k: The maxmum number of items can be queried at once.
+        get_time_budget: The maxmum amount of time spent copying data from 
+            shared memory to local memory. Increase this number for larger arrays.
+        put_desired_frequency: The maximum frequency that .put() can be called.
+            This influces the buffer size.
+        """
+
+        # create atomic counter
+        counter = SharedAtomicCounter(shm_manager)
+
+        # compute buffer size
+        # At any given moment, the past get_max_k items should never 
+        # be touched (to be read freely). Assuming the reading is reading
+        # these k items, which takes maximum of get_time_budget seconds,
+        # we need enough empty slots to make sure put_desired_frequency Hz
+        # of put can be sustaied.
+        buffer_size = int(np.ceil(
+            put_desired_frequency * get_time_budget 
+            * safety_margin)) + get_max_k
+
+        # allocate shared memory
+        shared_arrays = dict()
+        for spec in array_specs:
+            key = spec.name
+            assert key not in shared_arrays
+            array = SharedNDArray.create_from_shape(
+                mem_mgr=shm_manager,
+                shape=(buffer_size,) + tuple(spec.shape),
+                dtype=spec.dtype)
+            shared_arrays[key] = array
+        
+        # allocate timestamp array
+        timestamp_array = SharedNDArray.create_from_shape(
+            mem_mgr=shm_manager, 
+            shape=(buffer_size,),
+            dtype=np.float64)
+        timestamp_array.get()[:] = -np.inf
+        
+        self.buffer_size = buffer_size
+        self.array_specs = array_specs
+        self.counter = counter
+        self.shared_arrays = shared_arrays
+        self.timestamp_array = timestamp_array
+        self.get_time_budget = get_time_budget
+        self.get_max_k = get_max_k
+        self.put_desired_frequency = put_desired_frequency
+        self.ready_for_get = False
+
+    
+    @property
+    def count(self):
+        return self.counter.load()
+    
+    @classmethod
+    def create_from_examples(cls, 
+            shm_manager: SharedMemoryManager,
+            examples: Dict[str, Union[np.ndarray, numbers.Number]], 
+            get_max_k: int=32,
+            get_time_budget: float=0.01,
+            put_desired_frequency: float=60
+            ):
+        specs = list()
+        for key, value in examples.items():
+            shape = None
+            dtype = None
+            if isinstance(value, np.ndarray):
+                shape = value.shape
+                dtype = value.dtype
+                assert dtype != np.dtype('O')
+            elif isinstance(value, numbers.Number):
+                shape = tuple()
+                dtype = np.dtype(type(value))
+            else:
+                raise TypeError(f'Unsupported type {type(value)}')
+
+            spec = ArraySpec(
+                name=key,
+                shape=shape,
+                dtype=dtype
+            )
+            specs.append(spec)
+
+        obj = cls(
+            shm_manager=shm_manager,
+            array_specs=specs,
+            get_max_k=get_max_k,
+            get_time_budget=get_time_budget,
+            put_desired_frequency=put_desired_frequency
+            )
+        return obj
+
+    def clear(self):
+        self.counter.store(0)
+    
+    def put(self, data: Dict[str, Union[np.ndarray, numbers.Number]], wait: bool=True, serial_number: str='unknown'):
+        count = self.counter.load()
+        next_idx = count % self.buffer_size
+        # Make sure the next self.get_max_k elements in the ring buffer have at least 
+        # self.get_time_budget seconds untouched after written, so that
+        # get_last_k can safely read k elements from any count location.
+        # Sanity check: when get_max_k == 1, the element pointed by next_idx
+        # should be rewritten at minimum self.get_time_budget seconds later.
+        timestamp_lookahead_idx = (next_idx + self.get_max_k - 1) % self.buffer_size
+        old_timestamp = self.timestamp_array.get()[timestamp_lookahead_idx]
+        t = time.monotonic()
+        if (t - old_timestamp) < self.get_time_budget:
+            deltat = t - old_timestamp
+            if wait:
+                # sleep the remaining time to be safe
+                time.sleep(self.get_time_budget - deltat)
+            else:
+                if self.ready_for_get:
+                    # throw an error
+                    past_iters = self.buffer_size - self.get_max_k
+                    hz = past_iters / deltat
+                    raise TimeoutError(
+                        '[Camera {}] Put executed too fast {}items/{:.4f}s ~= {}Hz'.format(
+                            serial_number, past_iters, deltat,hz))
+
+        # write to shared memory
+        for key, value in data.items():
+            arr: np.ndarray
+            arr = self.shared_arrays[key].get()
+            if isinstance(value, np.ndarray):
+                arr[next_idx] = value
+            else:
+                arr[next_idx] = np.array(value, dtype=arr.dtype)
+        
+        # update timestamp
+        self.timestamp_array.get()[next_idx] = time.monotonic()
+        self.counter.add(1)
+
+    def _allocate_empty(self, k=None):
+        result = dict()
+        for spec in self.array_specs:
+            shape = spec.shape
+            if k is not None:
+                shape = (k,) + shape
+            result[spec.name] = np.empty(
+                shape=shape, dtype=spec.dtype)
+        return result
+
+    def get(self, out=None) -> Dict[str, np.ndarray]:
+        if out is None:
+            out = self._allocate_empty()
+        start_time = time.monotonic()
+        count = self.counter.load()
+        curr_idx = (count - 1) % self.buffer_size
+        for key, value in self.shared_arrays.items():
+            arr = value.get()
+            np.copyto(out[key], arr[curr_idx])
+        end_time = time.monotonic()
+        dt = end_time - start_time
+        if dt > self.get_time_budget:
+            raise TimeoutError(f'Get time out {dt} vs {self.get_time_budget}')
+        return out
+    
+    def get_last_k(self, k:int, out=None) -> Dict[str, np.ndarray]:
+        assert k <= self.get_max_k
+        if out is None:
+            out = self._allocate_empty(k)
+        start_time = time.monotonic()
+        count = self.counter.load()
+        assert k <= count
+        curr_idx = (count - 1) % self.buffer_size
+        for key, value in self.shared_arrays.items():
+            arr = value.get()
+            target = out[key]
+
+            end = curr_idx + 1
+            start = max(0, end - k)
+            target_end = k
+            target_start = target_end - (end - start)
+            target[target_start: target_end] = arr[start:end]
+
+            remainder = k - (end - start)
+            if remainder > 0:
+                # wrap around
+                end = self.buffer_size
+                start = end - remainder
+                target_start = 0
+                target_end = end - start
+                target[target_start: target_end] = arr[start:end]
+        end_time = time.monotonic()
+        dt = end_time - start_time
+        if dt > self.get_time_budget:
+            raise TimeoutError(f'Get time out {dt} vs {self.get_time_budget}')
+        return out
+
+    def get_all(self) -> Dict[str, np.ndarray]:
+        k = min(self.count, self.get_max_k)
+        return self.get_last_k(k=k)
diff --git a/src/experiments/real_world/camera/shared_memory/shared_memory_util.py b/src/experiments/real_world/camera/shared_memory/shared_memory_util.py
new file mode 100644
index 0000000000000000000000000000000000000000..6230118bc633734df15457074e7bd05cdf937809
--- /dev/null
+++ b/src/experiments/real_world/camera/shared_memory/shared_memory_util.py
@@ -0,0 +1,39 @@
+from typing import Tuple
+from dataclasses import dataclass
+import numpy as np
+from multiprocess.managers import SharedMemoryManager
+from atomics import atomicview, MemoryOrder, UINT
+
+@dataclass
+class ArraySpec:
+    name: str
+    shape: Tuple[int]
+    dtype: np.dtype
+
+
+class SharedAtomicCounter:
+    def __init__(self, 
+            shm_manager: SharedMemoryManager, 
+            size :int=8 # 64bit int
+            ):
+        shm = shm_manager.SharedMemory(size=size)
+        self.shm = shm
+        self.size = size
+        self.store(0) # initialize
+
+    @property
+    def buf(self):
+        return self.shm.buf[:self.size]
+
+    def load(self) -> int:
+        with atomicview(buffer=self.buf, atype=UINT) as a: 
+            value = a.load(order=MemoryOrder.ACQUIRE)
+        return value
+    
+    def store(self, value: int):
+        with atomicview(buffer=self.buf, atype=UINT) as a:
+            a.store(value, order=MemoryOrder.RELEASE)
+    
+    def add(self, value: int):
+        with atomicview(buffer=self.buf, atype=UINT) as a:
+            a.add(value, order=MemoryOrder.ACQ_REL)
diff --git a/src/experiments/real_world/camera/shared_memory/shared_ndarray.py b/src/experiments/real_world/camera/shared_memory/shared_ndarray.py
new file mode 100644
index 0000000000000000000000000000000000000000..72a1ba3d3b15cd11defdea9c427c69e7b00de16d
--- /dev/null
+++ b/src/experiments/real_world/camera/shared_memory/shared_ndarray.py
@@ -0,0 +1,166 @@
+from __future__ import annotations
+
+import multiprocess
+import multiprocess.synchronize
+from multiprocess.managers import SharedMemoryManager
+from multiprocess.shared_memory import SharedMemory
+from typing import Any, TYPE_CHECKING, Generic, Optional, Tuple, TypeVar, Union
+
+import numpy as np
+import numpy.typing as npt
+
+
+SharedMemoryLike = Union[str, SharedMemory]  # shared memory or name of shared memory
+SharedT = TypeVar("SharedT", bound=np.generic)
+
+
+class SharedNDArray(Generic[SharedT]):
+    """Class to keep track of and retrieve the data in a shared array
+    Attributes
+    ----------
+    shm
+        SharedMemory object containing the data of the array
+    shape
+        Shape of the NumPy array
+    dtype
+        Type of the NumPy array. Anything that may be passed to the `dtype=` argument in `np.ndarray`.
+    lock
+        (Optional) multiprocess.Lock to manage access to the SharedNDArray. This is only created if
+        lock=True is passed to the constructor, otherwise it is set to `None`.
+    A SharedNDArray object may be created either directly with a preallocated shared memory object plus the
+    dtype and shape of the numpy array it represents:
+    >>> from multiprocess.shared_memory import SharedMemory
+    >>> import numpy as np
+    >>> from shared_ndarray2 import SharedNDArray
+    >>> x = np.array([1, 2, 3])
+    >>> shm = SharedMemory(name="x", create=True, size=x.nbytes)
+    >>> arr = SharedNDArray(shm, x.shape, x.dtype)
+    >>> arr[:] = x[:]  # copy x into the array
+    >>> print(arr[:])
+    [1 2 3]
+    >>> shm.close()
+    >>> shm.unlink()
+    Or using a SharedMemoryManager either from an existing array or from arbitrary shape and nbytes:
+    >>> from multiprocess.managers import SharedMemoryManager
+    >>> mem_mgr = SharedMemoryManager()
+    >>> mem_mgr.start()  # Better yet, use SharedMemoryManager context manager
+    >>> arr = SharedNDArray.from_shape(mem_mgr, x.shape, x.dtype)
+    >>> arr[:] = x[:]  # copy x into the array
+    >>> print(arr[:])
+    [1 2 3]
+    >>> # -or in one step-
+    >>> arr = SharedNDArray.from_array(mem_mgr, x)
+    >>> print(arr[:])
+    [1 2 3]
+    `SharedNDArray` does not subclass numpy.ndarray but rather generates an ndarray on-the-fly in get(),
+    which is used in __getitem__ and __setitem__. Thus to access the data and/or use any ndarray methods
+    get() or __getitem__ or __setitem__ must be used
+    >>> arr.max()  # ERROR: SharedNDArray has no `max` method.
+    Traceback (most recent call last):
+        ....
+    AttributeError: SharedNDArray object has no attribute 'max'. To access NumPy ndarray object use .get() method.
+    >>> arr.get().max()  # (or arr[:].max())  OK: This gets an ndarray on which we can operate
+    3
+    >>> y = np.zeros(3)
+    >>> y[:] = arr  # ERROR: Cannot broadcast-assign a SharedNDArray to ndarray `y`
+    Traceback (most recent call last):
+        ...
+    ValueError: setting an array element with a sequence.
+    >>> y[:] = arr[:]  # OK: This gets an ndarray that can be copied element-wise to `y`
+    >>> mem_mgr.shutdown()
+    """
+
+    shm: SharedMemory
+    # shape: Tuple[int, ...]  # is a property
+    dtype: np.dtype
+    lock: Optional[multiprocess.synchronize.Lock]
+
+    def __init__(
+        self, shm: SharedMemoryLike, shape: Tuple[int, ...], dtype: npt.DTypeLike):
+        """Initialize a SharedNDArray object from existing shared memory, object shape, and dtype.
+        To initialize a SharedNDArray object from a memory manager and data or shape, use the `from_array()
+        or `from_shape()` classmethods.
+        Parameters
+        ----------
+        shm
+            `multiprocess.shared_memory.SharedMemory` object or name for connecting to an existing block
+            of shared memory (using SharedMemory constructor)
+        shape
+            Shape of the NumPy array to be represented in the shared memory
+        dtype
+            Data type for the NumPy array to be represented in shared memory. Any valid argument for
+            `np.dtype` may be used as it will be converted to an actual `dtype` object.
+        lock : bool, optional
+            If True, create a multiprocess.Lock object accessible with the `.lock` attribute, by default
+            False.  If passing the `SharedNDArray` as an argument to a `multiprocess.Pool` function this
+            should not be used -- see this comment to a Stack Overflow question about `multiprocess.Lock`:
+            https://stackoverflow.com/questions/25557686/python-sharing-a-lock-between-processes#comment72803059_25558333
+        Raises
+        ------
+        ValueError
+            The SharedMemory size (number of bytes) does not match the product of the shape and dtype
+            itemsize.
+        """
+        if isinstance(shm, str):
+            shm = SharedMemory(name=shm, create=False)
+        dtype = np.dtype(dtype)  # Try to convert to dtype
+        assert shm.size >= (dtype.itemsize * np.prod(shape))
+        self.shm = shm
+        self.dtype = dtype
+        self._shape: Tuple[int, ...] = shape
+
+    def __repr__(self):
+        # Like numpy's ndarray repr
+        cls_name = self.__class__.__name__
+        nspaces = len(cls_name) + 1
+        array_repr = str(self.get())
+        array_repr = array_repr.replace("\n", "\n" + " " * nspaces)
+        return f"{cls_name}({array_repr}, dtype={self.dtype})"
+
+    @classmethod
+    def create_from_array(
+        cls, mem_mgr: SharedMemoryManager, arr: npt.NDArray[SharedT]
+    ) -> SharedNDArray[SharedT]:
+        """Create a SharedNDArray from a SharedMemoryManager and an existing numpy array.
+        Parameters
+        ----------
+        mem_mgr
+            Running `multiprocess.managers.SharedMemoryManager` instance from which to create the
+            SharedMemory for the SharedNDArray
+        arr
+            NumPy `ndarray` object to copy into the created SharedNDArray upon initialization.
+        """
+        # Simply use from_shape() to create the SharedNDArray and copy the data into it.
+        shared_arr = cls.create_from_shape(mem_mgr, arr.shape, arr.dtype)
+        shared_arr.get()[:] = arr[:]
+        return shared_arr
+
+    @classmethod
+    def create_from_shape(
+        cls, mem_mgr: SharedMemoryManager, shape: Tuple, dtype: npt.DTypeLike) -> SharedNDArray:
+        """Create a SharedNDArray directly from a SharedMemoryManager
+        Parameters
+        ----------
+        mem_mgr
+            SharedMemoryManager instance that has been started
+        shape
+            Shape of the array
+        dtype
+            Data type for the NumPy array to be represented in shared memory. Any valid argument for
+            `np.dtype` may be used as it will be converted to an actual `dtype` object.
+        """
+        dtype = np.dtype(dtype)  # Convert to dtype if possible
+        shm = mem_mgr.SharedMemory(np.prod(shape) * dtype.itemsize)
+        return cls(shm=shm, shape=shape, dtype=dtype)
+
+    @property
+    def shape(self) -> Tuple[int, ...]:
+        return self._shape
+
+
+    def get(self) -> npt.NDArray[SharedT]:
+        """Get a numpy array with access to the shared memory"""
+        return np.ndarray(self.shape, dtype=self.dtype, buffer=self.shm.buf)
+
+    def __del__(self):
+        self.shm.close()
diff --git a/src/experiments/real_world/camera/single_realsense.py b/src/experiments/real_world/camera/single_realsense.py
new file mode 100644
index 0000000000000000000000000000000000000000..7ba3d2411ce9ddcc34bc0f2132ab794d482d3578
--- /dev/null
+++ b/src/experiments/real_world/camera/single_realsense.py
@@ -0,0 +1,450 @@
+from typing import Optional, Callable, Dict
+import os
+import enum
+import time
+import json
+import numpy as np
+import pyrealsense2 as rs
+import multiprocess as mp
+import cv2
+from threadpoolctl import threadpool_limits
+from multiprocess.managers import SharedMemoryManager
+
+from .utils import get_accumulate_timestamp_idxs
+from .shared_memory.shared_ndarray import SharedNDArray
+from .shared_memory.shared_memory_ring_buffer import SharedMemoryRingBuffer
+from .shared_memory.shared_memory_queue import SharedMemoryQueue, Full, Empty
+
+class Command(enum.Enum):
+    SET_COLOR_OPTION = 0
+    SET_DEPTH_OPTION = 1
+    START_RECORDING = 2
+    STOP_RECORDING = 3
+    RESTART_PUT = 4
+
+class SingleRealsense(mp.Process):
+    MAX_PATH_LENGTH = 4096 # linux path has a limit of 4096 bytes
+
+    def __init__(
+            self, 
+            shm_manager: SharedMemoryManager,
+            serial_number,
+            resolution=(1280,720),
+            capture_fps=30,
+            put_fps=None,
+            put_downsample=True,
+            enable_color=True,
+            enable_depth=False,
+            process_depth=False,
+            enable_infrared=False,
+            get_max_k=30,
+            advanced_mode_config=None,
+            transform: Optional[Callable[[Dict], Dict]] = None,
+            vis_transform: Optional[Callable[[Dict], Dict]] = None,
+            is_master=False,
+            verbose=False
+        ):
+        super().__init__()
+
+        if put_fps is None:
+            put_fps = capture_fps
+
+        # create ring buffer
+        resolution = tuple(resolution)
+        shape = resolution[::-1]
+        examples = dict()
+        if enable_color:
+            examples['color'] = np.empty(
+                shape=shape+(3,), dtype=np.uint8)
+        if enable_depth:
+            examples['depth'] = np.empty(
+                shape=shape, dtype=np.uint16)
+        if enable_infrared:
+            examples['infrared'] = np.empty(
+                shape=shape, dtype=np.uint8)
+        examples['camera_capture_timestamp'] = 0.0
+        examples['camera_receive_timestamp'] = 0.0
+        examples['timestamp'] = 0.0
+        examples['step_idx'] = 0
+
+        ring_buffer = SharedMemoryRingBuffer.create_from_examples(
+            shm_manager=shm_manager,
+            examples=examples if transform is None
+                else transform(dict(examples)),
+            get_max_k=get_max_k,
+            get_time_budget=0.2,
+            put_desired_frequency=put_fps
+        )
+
+        # create command queue
+        examples = {
+            'cmd': Command.SET_COLOR_OPTION.value,
+            'option_enum': rs.option.exposure.value,
+            'option_value': 0.0,
+            'put_start_time': 0.0
+        }
+
+        command_queue = SharedMemoryQueue.create_from_examples(
+            shm_manager=shm_manager,
+            examples=examples,
+            buffer_size=128
+        )
+
+        # create shared array for intrinsics
+        intrinsics_array = SharedNDArray.create_from_shape(
+                mem_mgr=shm_manager,
+                shape=(7,),
+                dtype=np.float64)
+        intrinsics_array.get()[:] = 0
+
+        # copied variables
+        self.serial_number = serial_number
+        self.resolution = resolution
+        self.capture_fps = capture_fps
+        self.put_fps = put_fps
+        self.put_downsample = put_downsample
+        self.enable_color = enable_color
+        self.enable_depth = enable_depth
+        self.enable_infrared = enable_infrared
+        self.advanced_mode_config = advanced_mode_config
+        self.transform = transform
+        self.vis_transform = vis_transform
+        self.process_depth = process_depth
+        self.is_master = is_master
+        self.verbose = verbose
+        self.put_start_time = None
+
+        # shared variables
+        self.stop_event = mp.Event()
+        self.ready_event = mp.Event()
+        self.ring_buffer = ring_buffer
+        self.command_queue = command_queue
+        self.intrinsics_array = intrinsics_array
+    
+    @staticmethod
+    def get_connected_devices_serial():
+        serials = list()
+        for d in rs.context().devices:
+            if d.get_info(rs.camera_info.name).lower() != 'platform camera':
+                serial = d.get_info(rs.camera_info.serial_number)
+                product_line = d.get_info(rs.camera_info.product_line)
+                if product_line == 'D400':
+                    # only works with D400 series
+                    serials.append(serial)
+        serials = sorted(serials)
+        return serials
+
+    # ========= context manager ===========
+    def __enter__(self):
+        self.start()
+        return self
+    
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        self.stop()
+
+    # ========= user API ===========
+    def start(self, wait=True, put_start_time=None):
+        self.put_start_time = put_start_time
+        super().start()
+        if wait:
+            self.start_wait()
+    
+    def stop(self, wait=True):
+        self.stop_event.set()
+        if wait:
+            self.end_wait()
+
+    def start_wait(self):
+        self.ready_event.wait()
+    
+    def end_wait(self):
+        self.join()
+
+    @property
+    def is_ready(self):
+        return self.ready_event.is_set()
+
+    def get(self, k=None, out=None):
+        if k is None:
+            return self.ring_buffer.get(out=out)
+        else:
+            return self.ring_buffer.get_last_k(k, out=out)
+    
+    # ========= user API ===========
+    def set_color_option(self, option: rs.option, value: float):
+        self.command_queue.put({
+            'cmd': Command.SET_COLOR_OPTION.value,
+            'option_enum': option.value,
+            'option_value': value
+        })
+    
+    def set_exposure(self, exposure=None, gain=None):
+        """
+        exposure: (1, 10000) 100us unit. (0.1 ms, 1/10000s)
+        gain: (0, 128)
+        """
+
+        if exposure is None and gain is None:
+            # auto exposure
+            self.set_color_option(rs.option.enable_auto_exposure, 1.0)
+        else:
+            # manual exposure
+            self.set_color_option(rs.option.enable_auto_exposure, 0.0)
+            if exposure is not None:
+                self.set_color_option(rs.option.exposure, exposure)
+            if gain is not None:
+                self.set_color_option(rs.option.gain, gain)
+    
+    def set_white_balance(self, white_balance=None):
+        if white_balance is None:
+            self.set_color_option(rs.option.enable_auto_white_balance, 1.0)
+        else:
+            self.set_color_option(rs.option.enable_auto_white_balance, 0.0)
+            self.set_color_option(rs.option.white_balance, white_balance)
+
+    def get_intrinsics(self):
+        assert self.ready_event.is_set()
+        fx, fy, ppx, ppy = self.intrinsics_array.get()[:4]
+        mat = np.eye(3)
+        mat[0,0] = fx
+        mat[1,1] = fy
+        mat[0,2] = ppx
+        mat[1,2] = ppy
+        return mat
+
+    def get_depth_scale(self):
+        assert self.ready_event.is_set()
+        scale = self.intrinsics_array.get()[-1]
+        return scale
+    
+    def depth_process(self, depth_frame):
+        depth_to_disparity = rs.disparity_transform(True)
+        disparity_to_depth = rs.disparity_transform(False)
+        
+        spatial = rs.spatial_filter()
+        spatial.set_option(rs.option.filter_magnitude, 5)
+        spatial.set_option(rs.option.filter_smooth_alpha, 0.75)
+        spatial.set_option(rs.option.filter_smooth_delta, 1)
+        spatial.set_option(rs.option.holes_fill, 1)
+        
+        temporal = rs.temporal_filter()
+        temporal.set_option(rs.option.filter_smooth_alpha, 0.75)
+        temporal.set_option(rs.option.filter_smooth_delta, 1)
+
+        filtered_depth = depth_to_disparity.process(depth_frame)
+        filtered_depth = spatial.process(filtered_depth)
+        filtered_depth = temporal.process(filtered_depth)
+        filtered_depth = disparity_to_depth.process(filtered_depth)
+        return filtered_depth
+
+    def restart_put(self, start_time):
+        self.command_queue.put({
+            'cmd': Command.RESTART_PUT.value,
+            'put_start_time': start_time
+        })
+     
+    # ========= interval API ===========
+    def run(self):
+        # limit threads
+        threadpool_limits(1)
+        cv2.setNumThreads(1)
+        w, h = self.resolution
+        fps = self.capture_fps
+        align = rs.align(rs.stream.color)
+        # Enable the streams from all the intel realsense devices
+        rs_config = rs.config()
+        if self.enable_color:
+            rs_config.enable_stream(rs.stream.color, 
+                w, h, rs.format.bgr8, fps)
+        if self.enable_depth:
+            rs_config.enable_stream(rs.stream.depth, 
+                w, h, rs.format.z16, fps)
+        if self.enable_infrared:
+            rs_config.enable_stream(rs.stream.infrared,
+                w, h, rs.format.y8, fps)
+        
+        def init_device():
+            rs_config.enable_device(self.serial_number)
+
+            # start pipeline
+            pipeline = rs.pipeline()
+            pipeline_profile = pipeline.start(rs_config)
+            self.pipeline = pipeline
+            self.pipeline_profile = pipeline_profile
+
+            # report global time
+            # https://github.com/IntelRealSense/librealsense/pull/3909
+            d = self.pipeline_profile.get_device().first_color_sensor()
+            d.set_option(rs.option.global_time_enabled, 1)
+
+            # setup advanced mode
+            if self.advanced_mode_config is not None:
+                json_text = json.dumps(self.advanced_mode_config)
+                device = self.pipeline_profile.get_device()
+                advanced_mode = rs.rs400_advanced_mode(device)
+                advanced_mode.load_json(json_text)
+
+            # get
+            color_stream = self.pipeline_profile.get_stream(rs.stream.color)
+            intr = color_stream.as_video_stream_profile().get_intrinsics()
+            order = ['fx', 'fy', 'ppx', 'ppy', 'height', 'width']
+            for i, name in enumerate(order):
+                self.intrinsics_array.get()[i] = getattr(intr, name)
+
+            if self.enable_depth:
+                depth_sensor = self.pipeline_profile.get_device().first_depth_sensor()
+                depth_scale = depth_sensor.get_depth_scale()
+                self.intrinsics_array.get()[-1] = depth_scale
+            
+            # one-time setup (intrinsics etc, ignore for now)
+            if self.verbose:
+                print(f'[SingleRealsense {self.serial_number}] Main loop started.')
+
+        try:
+            init_device()
+            # put frequency regulation
+            put_idx = None
+            put_start_time = self.put_start_time
+            if put_start_time is None:
+                put_start_time = time.time()
+
+            iter_idx = 0
+            t_start = time.time()
+            while not self.stop_event.is_set():
+                # wait for frames to come in
+                frameset = None
+                while frameset is None:
+                    try:
+                        frameset = self.pipeline.wait_for_frames()
+                    except RuntimeError as e:
+                        print(f'[SingleRealsense {self.serial_number}] Error: {e}. Ready state: {self.ready_event.is_set()}, Restarting device.')
+                        device = self.pipeline.get_active_profile().get_device()
+                        device.hardware_reset()
+                        self.pipeline.stop()
+                        init_device()
+                        continue
+                receive_time = time.time()
+                # align frames to color
+                frameset = align.process(frameset)
+
+                self.ring_buffer.ready_for_get = (receive_time - put_start_time >= 0)
+
+                # grab data
+                if self.verbose:
+                    grad_start_time = time.time()
+                data = dict()
+                data['camera_receive_timestamp'] = receive_time
+                # realsense report in ms
+                data['camera_capture_timestamp'] = frameset.get_timestamp() / 1000
+                if self.enable_color:
+                    # print(time.time())
+                    color_frame = frameset.get_color_frame()
+                    data['color'] = np.asarray(color_frame.get_data())
+                    t = color_frame.get_timestamp() / 1000
+                    data['camera_capture_timestamp'] = t
+                    # print('device', time.time() - t)
+                    # print(color_frame.get_frame_timestamp_domain())
+                if self.enable_depth:
+                    depth_frame = frameset.get_depth_frame()
+                    if self.process_depth:
+                        data['depth'] = self.depth_process(depth_frame)
+                    data['depth'] = np.asarray(depth_frame.get_data())
+                    # data['depth'] = np.asarray(
+                    #     frameset.get_depth_frame().get_data())
+                if self.enable_infrared:
+                    data['infrared'] = np.asarray(
+                        frameset.get_infrared_frame().get_data())
+                if self.verbose:
+                    print(f'[SingleRealsense {self.serial_number}] Grab data time {time.time() - grad_start_time}')
+                
+                # apply transform
+                if self.verbose:
+                    transform_start_time = time.time()
+                put_data = data
+                if self.transform is not None:
+                    put_data = self.transform(dict(data))
+                if self.verbose:
+                    print(f'[SingleRealsense {self.serial_number}] Transform time {time.time() - transform_start_time}')
+
+                if self.verbose:
+                    put_data_start_time = time.time()
+                if self.put_downsample:                
+                    # put frequency regulation
+                    # print(self.serial_number, put_start_time, put_idx, len(global_idxs))
+                    local_idxs, global_idxs, put_idx \
+                        = get_accumulate_timestamp_idxs(
+                            timestamps=[receive_time],
+                            start_time=put_start_time,
+                            dt=1/self.put_fps,
+                            # this is non in first iteration
+                            # and then replaced with a concrete number
+                            next_global_idx=put_idx,
+                            # continue to pump frames even if not started.
+                            # start_time is simply used to align timestamps.
+                            allow_negative=True
+                        )
+                    for step_idx in global_idxs:
+                        put_data['step_idx'] = step_idx
+                        # put_data['timestamp'] = put_start_time + step_idx / self.put_fps
+                        put_data['timestamp'] = receive_time
+                        # print(step_idx, data['timestamp'])
+                        self.ring_buffer.put(put_data, wait=False, serial_number=self.serial_number)
+                else:
+                    step_idx = int((receive_time - put_start_time) * self.put_fps)
+                    print(step_idx, receive_time)
+                    put_data['step_idx'] = step_idx
+                    put_data['timestamp'] = receive_time
+                    self.ring_buffer.put(put_data, wait=False, serial_number=self.serial_number)
+                if self.verbose:
+                    print(f'[SingleRealsense {self.serial_number}] Put data time {time.time() - put_data_start_time}', end=' ')
+                    print(f'with downsample for {len(global_idxs)}x' if self.put_downsample and len(global_idxs) > 1 else '')
+
+                # signal ready
+                if iter_idx == 0:
+                    self.ready_event.set()
+
+                # perf
+                t_end = time.time()
+                duration = t_end - t_start
+                frequency = np.round(1 / duration, 1)
+                t_start = t_end
+                if self.verbose:
+                    print(f'[SingleRealsense {self.serial_number}] FPS {frequency}')
+
+                # fetch command from queue
+                try:
+                    commands = self.command_queue.get_all()
+                    n_cmd = len(commands['cmd'])
+                except Empty:
+                    n_cmd = 0
+
+                # execute commands
+                for i in range(n_cmd):
+                    command = dict()
+                    for key, value in commands.items():
+                        command[key] = value[i]
+                    cmd = command['cmd']
+                    if cmd == Command.SET_COLOR_OPTION.value:
+                        sensor = self.pipeline_profile.get_device().first_color_sensor()
+                        option = rs.option(command['option_enum'])
+                        value = float(command['option_value'])
+                        sensor.set_option(option, value)
+                        # print('auto', sensor.get_option(rs.option.enable_auto_exposure))
+                        # print('exposure', sensor.get_option(rs.option.exposure))
+                        # print('gain', sensor.get_option(rs.option.gain))
+                    elif cmd == Command.SET_DEPTH_OPTION.value:
+                        sensor = self.pipeline_profile.get_device().first_depth_sensor().set_option(rs.option.inter_cam_sync_mode, 1 if self.is_master else 2)
+                        option = rs.option(command['option_enum'])
+                        value = float(command['option_value'])
+                        sensor.set_option(option, value)
+                    elif cmd == Command.RESTART_PUT.value:
+                        put_idx = None
+                        put_start_time = command['put_start_time']
+
+                iter_idx += 1
+        finally:
+            rs_config.disable_all_streams()
+            self.ready_event.set()
+        
+        if self.verbose:
+            print(f'[SingleRealsense {self.serial_number}] Exiting worker process.')
diff --git a/src/experiments/real_world/camera/utils.py b/src/experiments/real_world/camera/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..2e726728861305b98522863e4bddbe4051eacaf3
--- /dev/null
+++ b/src/experiments/real_world/camera/utils.py
@@ -0,0 +1,222 @@
+from typing import List, Tuple, Optional, Dict
+import math
+import numpy as np
+
+
+def get_accumulate_timestamp_idxs(
+    timestamps: List[float],  
+    start_time: float, 
+    dt: float, 
+    eps:float=1e-5,
+    next_global_idx: Optional[int]=0,
+    allow_negative=False
+    ) -> Tuple[List[int], List[int], int]:
+    """
+    For each dt window, choose the first timestamp in the window.
+    Assumes timestamps sorted. One timestamp might be chosen multiple times due to dropped frames.
+    next_global_idx should start at 0 normally, and then use the returned next_global_idx. 
+    However, when overwiting previous values are desired, set last_global_idx to None.
+
+    Returns:
+    local_idxs: which index in the given timestamps array to chose from
+    global_idxs: the global index of each chosen timestamp
+    next_global_idx: used for next call.
+    """
+    local_idxs = list()
+    global_idxs = list()
+    for local_idx, ts in enumerate(timestamps):
+        # add eps * dt to timestamps so that when ts == start_time + k * dt 
+        # is always recorded as kth element (avoiding floating point errors)
+        global_idx = math.floor((ts - start_time) / dt + eps)
+        if (not allow_negative) and (global_idx < 0):
+            continue
+        if next_global_idx is None:
+            next_global_idx = global_idx
+
+        n_repeats = max(0, global_idx - next_global_idx + 1)
+        for i in range(n_repeats):
+            local_idxs.append(local_idx)
+            global_idxs.append(next_global_idx + i)
+        next_global_idx += n_repeats
+    return local_idxs, global_idxs, next_global_idx
+
+
+def align_timestamps(    
+        timestamps: List[float], 
+        target_global_idxs: List[int], 
+        start_time: float, 
+        dt: float, 
+        eps:float=1e-5):
+    if isinstance(target_global_idxs, np.ndarray):
+        target_global_idxs = target_global_idxs.tolist()
+    assert len(target_global_idxs) > 0
+
+    local_idxs, global_idxs, _ = get_accumulate_timestamp_idxs(
+        timestamps=timestamps,
+        start_time=start_time,
+        dt=dt,
+        eps=eps,
+        next_global_idx=target_global_idxs[0],
+        allow_negative=True
+    )
+    if len(global_idxs) > len(target_global_idxs):
+        # if more steps available, truncate
+        global_idxs = global_idxs[:len(target_global_idxs)]
+        local_idxs = local_idxs[:len(target_global_idxs)]
+    
+    if len(global_idxs) == 0:
+        import pdb; pdb.set_trace()
+
+    for i in range(len(target_global_idxs) - len(global_idxs)):
+        # if missing, repeat
+        local_idxs.append(len(timestamps)-1)
+        global_idxs.append(global_idxs[-1] + 1)
+    assert global_idxs == target_global_idxs
+    assert len(local_idxs) == len(global_idxs)
+    return local_idxs
+
+
+class TimestampObsAccumulator:
+    def __init__(self, 
+            start_time: float, 
+            dt: float, 
+            eps: float=1e-5):
+        self.start_time = start_time
+        self.dt = dt
+        self.eps = eps
+        self.obs_buffer = dict()
+        self.timestamp_buffer = None
+        self.next_global_idx = 0
+    
+    def __len__(self):
+        return self.next_global_idx
+    
+    @property
+    def data(self):
+        if self.timestamp_buffer is None:
+            return dict()
+        result = dict()
+        for key, value in self.obs_buffer.items():
+            result[key] = value[:len(self)]
+        return result
+
+    @property
+    def actual_timestamps(self):
+        if self.timestamp_buffer is None:
+            return np.array([])
+        return self.timestamp_buffer[:len(self)]
+    
+    @property
+    def timestamps(self):
+        if self.timestamp_buffer is None:
+            return np.array([])
+        return self.start_time + np.arange(len(self)) * self.dt
+
+    def put(self, data: Dict[str, np.ndarray], timestamps: np.ndarray):
+        """
+        data:
+            key: T,*
+        """
+
+        local_idxs, global_idxs, self.next_global_idx = get_accumulate_timestamp_idxs(
+            timestamps=timestamps,
+            start_time=self.start_time,
+            dt=self.dt,
+            eps=self.eps,
+            next_global_idx=self.next_global_idx
+        )
+
+        if len(global_idxs) > 0:
+            if self.timestamp_buffer is None:
+                # first allocation
+                self.obs_buffer = dict()
+                for key, value in data.items():
+                    self.obs_buffer[key] = np.zeros_like(value)
+                self.timestamp_buffer = np.zeros(
+                    (len(timestamps),), dtype=np.float64)
+            
+            this_max_size = global_idxs[-1] + 1
+            if this_max_size > len(self.timestamp_buffer):
+                # reallocate
+                new_size = max(this_max_size, len(self.timestamp_buffer) * 2)
+                for key in list(self.obs_buffer.keys()):
+                    new_shape = (new_size,) + self.obs_buffer[key].shape[1:]
+                    self.obs_buffer[key] = np.resize(self.obs_buffer[key], new_shape)
+                self.timestamp_buffer = np.resize(self.timestamp_buffer, (new_size))
+            
+            # write data
+            for key, value in self.obs_buffer.items():
+                value[global_idxs] = data[key][local_idxs]
+            self.timestamp_buffer[global_idxs] = timestamps[local_idxs]
+
+
+class TimestampActionAccumulator:
+    def __init__(self, 
+            start_time: float, 
+            dt: float, 
+            eps: float=1e-5):
+        """
+        Different from Obs accumulator, the action accumulator
+        allows overwriting previous values.
+        """
+        self.start_time = start_time
+        self.dt = dt
+        self.eps = eps
+        self.action_buffer = None
+        self.timestamp_buffer = None
+        self.size = 0
+    
+    def __len__(self):
+        return self.size
+    
+    @property
+    def actions(self):
+        if self.action_buffer is None:
+            return np.array([])
+        return self.action_buffer[:len(self)]
+    
+    @property
+    def actual_timestamps(self):
+        if self.timestamp_buffer is None:
+            return np.array([])
+        return self.timestamp_buffer[:len(self)]
+    
+    @property
+    def timestamps(self):
+        if self.timestamp_buffer is None:
+            return np.array([])
+        return self.start_time + np.arange(len(self)) * self.dt
+
+    def put(self, actions: np.ndarray, timestamps: np.ndarray):
+        """
+        Note: timestamps is the time when the action will be issued, 
+        not when the action will be completed (target_timestamp)
+        """
+
+        local_idxs, global_idxs, _ = get_accumulate_timestamp_idxs(
+            timestamps=timestamps,
+            start_time=self.start_time,
+            dt=self.dt,
+            eps=self.eps,
+            # allows overwriting previous actions
+            next_global_idx=None
+        )
+
+        if len(global_idxs) > 0:
+            if self.timestamp_buffer is None:
+                # first allocation
+                self.action_buffer = np.zeros_like(actions)
+                self.timestamp_buffer = np.zeros((len(actions),), dtype=np.float64)
+
+            this_max_size = global_idxs[-1] + 1
+            if this_max_size > len(self.timestamp_buffer):
+                # reallocate
+                new_size = max(this_max_size, len(self.timestamp_buffer) * 2)
+                new_shape = (new_size,) + self.action_buffer.shape[1:]
+                self.action_buffer = np.resize(self.action_buffer, new_shape)
+                self.timestamp_buffer = np.resize(self.timestamp_buffer, (new_size,))
+            
+            # potentially rewrite old data (as expected)
+            self.action_buffer[global_idxs] = actions[local_idxs]
+            self.timestamp_buffer[global_idxs] = timestamps[local_idxs]
+            self.size = max(self.size, this_max_size)
diff --git a/src/experiments/real_world/gs/config/default.yaml b/src/experiments/real_world/gs/config/default.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..0d202389e38350c8eeab9a6ffeec649eafce9689
--- /dev/null
+++ b/src/experiments/real_world/gs/config/default.yaml
@@ -0,0 +1,12 @@
+# training
+weight_im: 1.0
+weight_seg: 3.0
+grad_thresh: 0.0002
+remove_threshold: 0.005
+remove_thresh_5k: 0.25
+scale_scene_radius: 0.05
+num_iters: 10000
+
+# rendering
+near: 0.01
+far: 100
\ No newline at end of file
diff --git a/src/experiments/real_world/gs/convert.py b/src/experiments/real_world/gs/convert.py
new file mode 100644
index 0000000000000000000000000000000000000000..6c1d2a9bef92120fa7365bbb07d5e4b9a0d457f6
--- /dev/null
+++ b/src/experiments/real_world/gs/convert.py
@@ -0,0 +1,76 @@
+import numpy as np
+from io import BytesIO
+
+
+def quat_mult(q1, q2):
+    w1, x1, y1, z1 = q1
+    w2, x2, y2, z2 = q2
+    w = w1 * w2 - x1 * x2 - y1 * y2 - z1 * z2
+    x = w1 * x2 + x1 * w2 + y1 * z2 - z1 * y2
+    y = w1 * y2 - x1 * z2 + y1 * w2 + z1 * x2
+    z = w1 * z2 + x1 * y2 - y1 * x2 + z1 * w2
+    return np.array([w, x, y, z], dtype=np.float32)
+
+
+def rot_mat_to_quat(rot_mat):
+    w = np.sqrt(1 + rot_mat[0, 0] + rot_mat[1, 1] + rot_mat[2, 2]) / 2
+    x = (rot_mat[2, 1] - rot_mat[1, 2]) / (4 * w)
+    y = (rot_mat[0, 2] - rot_mat[2, 0]) / (4 * w)
+    z = (rot_mat[1, 0] - rot_mat[0, 1]) / (4 * w)
+    return np.array([w, x, y, z], dtype=np.float32)
+
+
+def save_to_splat(pts, colors, scales, quats, opacities, output_file, center=True, rotate=True, rot_rev=False):
+    if center:
+        pts_mean = np.mean(pts, axis=0)
+        pts = pts - pts_mean
+    buffer = BytesIO()
+    for (v, c, s, q, o) in zip(pts, colors, scales, quats, opacities):
+        position = np.array([v[0], v[1], v[2]], dtype=np.float32)
+        scales = np.array([s[0], s[1], s[2]], dtype=np.float32)
+        rot = np.array([q[0], q[1], q[2], q[3]], dtype=np.float32)
+        # SH_C0 = 0.28209479177387814
+        # color = np.array([0.5 + SH_C0 * c[0], 0.5 + SH_C0 * c[1], 0.5 + SH_C0 * c[2], o[0]])
+        color = np.array([c[0], c[1], c[2], o[0]])
+
+        # rotate around x axis
+        if rotate:
+            rot_x_90 = np.array([[1, 0, 0], [0, 0, -1], [0, 1, 0]], dtype=np.float32)
+            if not rot_rev:
+                rot_x_90 = np.linalg.inv(rot_x_90)
+            position = np.dot(rot_x_90, position)
+            rot = quat_mult(rot_mat_to_quat(rot_x_90), rot)
+
+        buffer.write(position.tobytes())
+        buffer.write(scales.tobytes())
+        buffer.write((color * 255).clip(0, 255).astype(np.uint8).tobytes())
+        buffer.write(
+            ((rot / np.linalg.norm(rot)) * 128 + 128)
+            .clip(0, 255)
+            .astype(np.uint8)
+            .tobytes()
+        )
+    with open(output_file, "wb") as f:
+        f.write(buffer.getvalue())
+
+
+def read_splat(splat_file):
+    with open(splat_file, "rb") as f:
+        data = f.read()
+    pts = []
+    colors = []
+    scales = []
+    quats = []
+    opacities = []
+    for i in range(0, len(data), 32):
+        v = np.frombuffer(data[i : i + 12], dtype=np.float32)
+        s = np.frombuffer(data[i + 12 : i + 24], dtype=np.float32)
+        c = np.frombuffer(data[i + 24 : i + 28], dtype=np.uint8) / 255
+        q = np.frombuffer(data[i + 28 : i + 32], dtype=np.uint8)
+        q = (q * 1.0 - 128) / 128
+        pts.append(v)
+        scales.append(s)
+        colors.append(c[:3])
+        quats.append(q)
+        opacities.append(c[3:])
+    return np.array(pts), np.array(colors), np.array(scales), np.array(quats), np.array(opacities)
diff --git a/src/experiments/real_world/gs/external.py b/src/experiments/real_world/gs/external.py
new file mode 100644
index 0000000000000000000000000000000000000000..c7894e402dd24f4910249503272d168851cd1487
--- /dev/null
+++ b/src/experiments/real_world/gs/external.py
@@ -0,0 +1,296 @@
+
+"""
+# Copyright (C) 2023, Inria
+# GRAPHDECO research group, https://team.inria.fr/graphdeco
+# All rights reserved.
+#
+# This software is free for non-commercial, research and evaluation use
+# under the terms of the LICENSE.md file found here:
+# https://github.com/graphdeco-inria/gaussian-splatting/blob/main/LICENSE.md
+#
+# For inquiries contact  george.drettakis@inria.fr
+
+#######################################################################################################################
+##### NOTE: CODE IN THIS FILE IS NOT INCLUDED IN THE OVERALL PROJECT'S MIT LICENSE #####
+##### USE OF THIS CODE FOLLOWS THE COPYRIGHT NOTICE ABOVE #####
+#######################################################################################################################
+"""
+
+import torch
+import torch.nn.functional as func
+from torch.autograd import Variable
+from math import exp
+
+
+def build_rotation(q):
+    norm = torch.sqrt(q[:, 0] * q[:, 0] + q[:, 1] * q[:, 1] + q[:, 2] * q[:, 2] + q[:, 3] * q[:, 3])
+    q = q / norm[:, None]
+    rot = torch.zeros((q.size(0), 3, 3), device='cuda')
+    r = q[:, 0]
+    x = q[:, 1]
+    y = q[:, 2]
+    z = q[:, 3]
+    rot[:, 0, 0] = 1 - 2 * (y * y + z * z)
+    rot[:, 0, 1] = 2 * (x * y - r * z)
+    rot[:, 0, 2] = 2 * (x * z + r * y)
+    rot[:, 1, 0] = 2 * (x * y + r * z)
+    rot[:, 1, 1] = 1 - 2 * (x * x + z * z)
+    rot[:, 1, 2] = 2 * (y * z - r * x)
+    rot[:, 2, 0] = 2 * (x * z - r * y)
+    rot[:, 2, 1] = 2 * (y * z + r * x)
+    rot[:, 2, 2] = 1 - 2 * (x * x + y * y)
+    return rot
+
+
+def calc_mse(img1, img2):
+    return ((img1 - img2) ** 2).view(img1.shape[0], -1).mean(1, keepdim=True)
+
+
+def calc_psnr(img1, img2):
+    mse = ((img1 - img2) ** 2).view(img1.shape[0], -1).mean(1, keepdim=True)
+    return 20 * torch.log10(1.0 / torch.sqrt(mse))
+
+
+def gaussian(window_size, sigma):
+    """
+    Generate a 1D Gaussian kernel.
+
+    Parameters:
+    - window_size: The size (length) of the output Gaussian kernel.
+    - sigma: The standard deviation of the Gaussian distribution.
+
+    Returns:
+    - A 1D tensor representing the Gaussian kernel normalized to have a sum of 1.
+    """
+
+    # For each position in the desired window size, calculate the Gaussian value. 
+    # The middle of the window corresponds to the peak of the Gaussian.
+    gauss = torch.Tensor([exp(-(x - window_size // 2) ** 2 / float(2 * sigma ** 2)) for x in range(window_size)])
+    
+    # Normalize the Gaussian kernel to have a sum of 1 and return it.
+    return gauss / gauss.sum()
+
+
+def create_window(window_size, channel):
+    """
+    Generate a 2D Gaussian kernel window.
+
+    Parameters:
+    - window_size: The size (width and height) of the output 2D Gaussian kernel.
+    - channel: Number of channels for which the window will be replicated.
+
+    Returns:
+    - A 4D tensor representing the Gaussian window for the specified number of channels.
+    """
+
+    # Create a 1D Gaussian kernel of size 'window_size' with standard deviation 1.5.
+    # The unsqueeze operation adds an extra dimension, making it a 2D tensor.
+    _1D_window = gaussian(window_size, 1.5).unsqueeze(1)
+
+    # Compute the outer product of the 1D Gaussian kernel with itself to get a 2D Gaussian kernel.
+    # This results in a symmetric 2D Gaussian kernel.
+    _2D_window = _1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0)
+
+    # Expand the 2D window to have the desired number of channels.
+    # The expand operation replicates the 2D window for each channel.
+    window = Variable(_2D_window.expand(channel, 1, window_size, window_size).contiguous())
+
+    return window
+
+
+def calc_ssim(img1, img2, window_size=11, size_average=True):
+    channel = img1.size(-3)
+    window = create_window(window_size, channel)
+    # print('img1', img1.device)
+    # window = window.to(device=img1.device)
+    # assert torch.isfinite(img1).all(), "img1 contains NaN or Inf"
+    # assert torch.isfinite(window).all(), "window contains NaN or Inf"
+    if img1.is_cuda:
+        window = window.cuda(img1.get_device())
+    window = window.type_as(img1)
+
+    return _ssim(img1, img2, window, window_size, channel, size_average)
+
+
+def _ssim(img1, img2, window, window_size, channel, size_average=True):
+    mu1 = func.conv2d(img1, window, padding=window_size // 2, groups=channel)
+    mu2 = func.conv2d(img2, window, padding=window_size // 2, groups=channel)
+
+    mu1_sq = mu1.pow(2)
+    mu2_sq = mu2.pow(2)
+    mu1_mu2 = mu1 * mu2
+
+    sigma1_sq = func.conv2d(img1 * img1, window, padding=window_size // 2, groups=channel) - mu1_sq
+    sigma2_sq = func.conv2d(img2 * img2, window, padding=window_size // 2, groups=channel) - mu2_sq
+    sigma12 = func.conv2d(img1 * img2, window, padding=window_size // 2, groups=channel) - mu1_mu2
+
+    c1 = 0.01 ** 2
+    c2 = 0.03 ** 2
+
+    ssim_map = ((2 * mu1_mu2 + c1) * (2 * sigma12 + c2)) / ((mu1_sq + mu2_sq + c1) * (sigma1_sq + sigma2_sq + c2))
+
+    if size_average:
+        return ssim_map.mean()
+    else:
+        return ssim_map.mean(1).mean(1).mean(1)
+
+
+def accumulate_mean2d_gradient(variables):
+    variables['means2D_gradient_accum'][variables['seen']] += torch.norm(
+        variables['means2D'].grad[variables['seen'], :2], dim=-1)
+    variables['denom'][variables['seen']] += 1
+    return variables
+
+
+def update_params_and_optimizer(new_params, params, optimizer):
+    for k, v in new_params.items():
+        group = [x for x in optimizer.param_groups if x["name"] == k][0]
+        stored_state = optimizer.state.get(group['params'][0], None)
+
+        stored_state["exp_avg"] = torch.zeros_like(v)
+        stored_state["exp_avg_sq"] = torch.zeros_like(v)
+        del optimizer.state[group['params'][0]]
+
+        group["params"][0] = torch.nn.Parameter(v.requires_grad_(True))
+        optimizer.state[group['params'][0]] = stored_state
+        params[k] = group["params"][0]
+    return params
+
+
+def cat_params_to_optimizer(new_params, params, optimizer):
+    for k, v in new_params.items():
+        group = [g for g in optimizer.param_groups if g['name'] == k][0]
+        stored_state = optimizer.state.get(group['params'][0], None)
+        if stored_state is not None:
+            stored_state["exp_avg"] = torch.cat((stored_state["exp_avg"], torch.zeros_like(v)), dim=0)
+            stored_state["exp_avg_sq"] = torch.cat((stored_state["exp_avg_sq"], torch.zeros_like(v)), dim=0)
+            del optimizer.state[group['params'][0]]
+            group["params"][0] = torch.nn.Parameter(torch.cat((group["params"][0], v), dim=0).requires_grad_(True))
+            optimizer.state[group['params'][0]] = stored_state
+            params[k] = group["params"][0]
+        else:
+            group["params"][0] = torch.nn.Parameter(torch.cat((group["params"][0], v), dim=0).requires_grad_(True))
+            params[k] = group["params"][0]
+    return params
+
+
+def remove_points(to_remove, params, variables, optimizer):
+    """
+
+    Parameters:
+    - to_remove: A boolean tensor where 'True' indicates the points to remove.
+    - params: A dictionary containing parameters.
+    - variables: A dictionary containing various variables.
+    - optimizer: An optimizer object containing optimization information.
+
+    Returns:
+    - Updated params and variables dictionaries after removal.
+    """
+    
+    # Find the points that we want to keep (the opposite of `to_remove`).
+    to_keep = ~to_remove
+    
+    # Extract the keys from `params` except for 'cam_m' and 'cam_c'.
+    keys = [k for k in params.keys() if k not in ['cam_m', 'cam_c']]
+    
+    for k in keys:
+        # Find the parameter group associated with the current key in the optimizer.
+        group = [g for g in optimizer.param_groups if g['name'] == k][0]
+        
+        # Try to get the state of this group from the optimizer (this contains momentum information, etc. for optimizers like Adam).
+        stored_state = optimizer.state.get(group['params'][0], None)
+        
+        if stored_state is not None:
+            # Update the stored state by keeping only the desired entries.
+            stored_state["exp_avg"] = stored_state["exp_avg"][to_keep]
+            stored_state["exp_avg_sq"] = stored_state["exp_avg_sq"][to_keep]
+            
+            # Delete the old state and set a new parameter tensor, keeping only the desired entries and ensuring gradients can be computed.
+            del optimizer.state[group['params'][0]]
+            group["params"][0] = torch.nn.Parameter((group["params"][0][to_keep].requires_grad_(True)))
+            optimizer.state[group['params'][0]] = stored_state
+            params[k] = group["params"][0]
+        else:
+            # If there's no stored state, just update the parameter tensor.
+            group["params"][0] = torch.nn.Parameter(group["params"][0][to_keep].requires_grad_(True))
+            params[k] = group["params"][0]
+
+    variables['means2D_gradient_accum'] = variables['means2D_gradient_accum'][to_keep]
+    variables['denom'] = variables['denom'][to_keep]
+    variables['max_2D_radius'] = variables['max_2D_radius'][to_keep]
+    
+    return params, variables
+
+
+def inverse_sigmoid(x):
+    return torch.log(x / (1 - x))
+
+
+def densify(params, variables, optimizer, i, grad_thresh, remove_thresh, remove_thresh_5k, scale_scene_radius):
+    """
+    Adjusts the density of points based on various conditions and thresholds.
+
+    Parameters:
+    - params: A dictionary containing parameters.
+    - variables: A dictionary containing various variables.
+    - optimizer: An optimizer object containing optimization information.
+    - i: An iteration or step count.
+    - remove_thresh: A threshold for removing points.
+
+    Returns:
+    - Updated params and variables dictionaries after adjustment.
+    """
+    if i <= 5000:
+        variables = accumulate_mean2d_gradient(variables)
+        if (i >= 500) and (i % 100 == 0):
+            # Calculate the gradient of the means2D values and handle NaNs.
+            grads = variables['means2D_gradient_accum'] / variables['denom']
+            grads[grads.isnan()] = 0.0
+            # Define points that should be cloned based on gradient thresholds and scales of the points.
+            to_clone = torch.logical_and(grads >= grad_thresh, (
+                        torch.max(torch.exp(params['log_scales']), dim=1).values <= scale_scene_radius * variables['scene_radius']))
+            # Extract parameters for points that need cloning.
+            new_params = {k: v[to_clone] for k, v in params.items() if k not in ['cam_m', 'cam_c']}
+            params = cat_params_to_optimizer(new_params, params, optimizer)
+            num_pts = params['means3D'].shape[0]
+
+            padded_grad = torch.zeros(num_pts, device="cuda")
+            padded_grad[:grads.shape[0]] = grads
+            to_split = torch.logical_and(padded_grad >= grad_thresh,
+                                         torch.max(torch.exp(params['log_scales']), dim=1).values > scale_scene_radius * variables[
+                                             'scene_radius'])
+            n = 2  # number to split into
+            new_params = {k: v[to_split].repeat(n, 1) for k, v in params.items() if k not in ['cam_m', 'cam_c']}
+            stds = torch.exp(params['log_scales'])[to_split].repeat(n, 1)
+            means = torch.zeros((stds.size(0), 3), device="cuda")
+            samples = torch.normal(mean=means, std=stds)
+            rots = build_rotation(params['unnorm_rotations'][to_split]).repeat(n, 1, 1)
+            new_params['means3D'] += torch.bmm(rots, samples.unsqueeze(-1)).squeeze(-1)
+            new_params['log_scales'] = torch.log(torch.exp(new_params['log_scales']) / (0.8 * n))
+            params = cat_params_to_optimizer(new_params, params, optimizer)
+            num_pts = params['means3D'].shape[0]
+
+            variables['means2D_gradient_accum'] = torch.zeros(num_pts, device="cuda")
+            variables['denom'] = torch.zeros(num_pts, device="cuda")
+            variables['max_2D_radius'] = torch.zeros(num_pts, device="cuda")
+            to_remove = torch.cat((to_split, torch.zeros(n * to_split.sum(), dtype=torch.bool, device="cuda")))
+            params, variables = remove_points(to_remove, params, variables, optimizer)
+
+            remove_threshold = remove_thresh_5k if i == 5000 else remove_thresh
+            to_remove = (torch.sigmoid(params['logit_opacities']) < remove_threshold).squeeze()
+            # print("num of to remove: ", to_remove.sum())
+            if i >= 3000:
+                big_points_ws = torch.exp(params['log_scales']).max(dim=1).values > 0.1 * variables['scene_radius']
+                # print("num of big points: ", big_points_ws.sum())
+                to_remove = torch.logical_or(to_remove, big_points_ws)
+            params, variables = remove_points(to_remove, params, variables, optimizer)
+            
+            torch.cuda.empty_cache()
+
+        if i > 0 and i % 3000 == 0:
+            new_params = {'logit_opacities': inverse_sigmoid(torch.ones_like(params['logit_opacities']) * 0.01)}
+            params = update_params_and_optimizer(new_params, params, optimizer)
+
+    num_pts = params['means3D'].shape[0]
+
+    return params, variables, num_pts
diff --git a/src/experiments/real_world/gs/helpers.py b/src/experiments/real_world/gs/helpers.py
new file mode 100644
index 0000000000000000000000000000000000000000..d3e71fafa9ff87b98becf92e5fb361fafc9b27ca
--- /dev/null
+++ b/src/experiments/real_world/gs/helpers.py
@@ -0,0 +1,212 @@
+import torch
+import os
+import open3d as o3d
+import numpy as np
+from diff_gaussian_rasterization import GaussianRasterizationSettings as Camera
+
+
+def setup_camera(w, h, k, w2c, near=0.01, far=100.0, bg=[0, 0, 0], z_threshold=0.2, device='cuda'):
+    fx, fy, cx, cy = k[0][0], k[1][1], k[0][2], k[1][2]
+    w2c = torch.tensor(w2c).cuda().float()
+    cam_center = torch.inverse(w2c)[:3, 3]
+    w2c = w2c.unsqueeze(0).transpose(1, 2)
+    opengl_proj = torch.tensor([[2 * fx / w, 0.0, -(w - 2 * cx) / w, 0.0],
+                                [0.0, 2 * fy / h, -(h - 2 * cy) / h, 0.0],
+                                [0.0, 0.0, far / (far - near), -(far * near) / (far - near)],
+                                [0.0, 0.0, 1.0, 0.0]]).cuda().float().unsqueeze(0).transpose(1, 2)
+    full_proj = w2c.bmm(opengl_proj)
+    cam = Camera(
+        image_height=h,
+        image_width=w,
+        tanfovx=w / (2 * fx),
+        tanfovy=h / (2 * fy),
+        bg=torch.tensor(bg, dtype=torch.float32, device=device),
+        scale_modifier=1.0,
+        viewmatrix=w2c.to(device),
+        projmatrix=full_proj.to(device),
+        sh_degree=0,
+        campos=cam_center.to(device),
+        prefiltered=False,
+        z_threshold=z_threshold,
+    )
+    return cam
+
+
+def params2rendervar(params):
+    rendervar = {
+        'means3D': params['means3D'],
+        'colors_precomp': params['rgb_colors'],
+        'rotations': torch.nn.functional.normalize(params['unnorm_rotations']),
+        'opacities': torch.sigmoid(params['logit_opacities']),
+        'scales': torch.exp(params['log_scales']),
+        'means2D': torch.zeros_like(params['means3D'], requires_grad=True, device="cuda") + 0
+    }
+    return rendervar
+
+def params2rendervar_wt(params, t):
+    print(params['unnorm_rotations'][t])
+    rendervar = {
+        'means3D': params['means3D'][t],
+        'colors_precomp': params['rgb_colors'][t],
+        'rotations': torch.nn.functional.normalize(params['unnorm_rotations'][t]),
+        'opacities': torch.sigmoid(params['logit_opacities'][t]),
+        'scales': torch.exp(params['log_scales'][t]),
+        'means2D': torch.zeros_like(params['means3D'][t], requires_grad=True, device="cuda") + 0
+    }
+    return rendervar
+
+def params2rendervar_consistent_rgb(params, variables):
+    rendervar = {
+        'means3D': params['means3D'],
+        'colors_precomp': variables['rgb_colors'],
+        'rotations': torch.nn.functional.normalize(params['unnorm_rotations']),
+        'opacities': torch.sigmoid(params['logit_opacities']),
+        'scales': torch.exp(params['log_scales']),
+        'means2D': torch.zeros_like(params['means3D'], requires_grad=True, device="cuda") + 0
+    }
+    return rendervar
+
+
+def l1_loss_v1(x, y):
+    return torch.abs((x - y)).mean()
+
+
+def l1_loss_v2(x, y):
+    return (torch.abs(x - y).sum(-1)).mean()
+
+
+def weighted_l2_loss_v1(x, y, w):
+    return torch.sqrt(((x - y) ** 2) * w + 1e-20).mean()
+
+
+def weighted_l2_loss_v2(x, y, w):
+    return torch.sqrt(((x - y) ** 2).sum(-1) * w + 1e-20).mean()
+
+
+def quat_mult(q1, q2):
+    w1, x1, y1, z1 = q1.T
+    w2, x2, y2, z2 = q2.T
+    w = w1 * w2 - x1 * x2 - y1 * y2 - z1 * z2
+    x = w1 * x2 + x1 * w2 + y1 * z2 - z1 * y2
+    y = w1 * y2 - x1 * z2 + y1 * w2 + z1 * x2
+    z = w1 * z2 + x1 * y2 - y1 * x2 + z1 * w2
+    return torch.stack([w, x, y, z]).T
+
+
+def o3d_knn(pts, num_knn):
+    indices = []
+    sq_dists = []
+    pcd = o3d.geometry.PointCloud()
+    # breakpoint()
+    # print(pts.shape)
+    pts_cont = np.ascontiguousarray(pts, np.float64)
+    # print(pts_cont.shape)
+    pcd.points = o3d.utility.Vector3dVector(np.ascontiguousarray(pts, np.float64))
+    if len(pcd.points) == 0:
+        print("Point cloud is empty!")
+    else:
+        pcd_tree = o3d.geometry.KDTreeFlann(pcd)
+
+    for p in pcd.points:
+        [_, i, d] = pcd_tree.search_knn_vector_3d(p, num_knn + 1)
+        indices.append(i[1:])
+        sq_dists.append(d[1:])
+    return np.array(sq_dists), np.array(indices)
+
+def o3d_knn_tensor(pts_tensor, num_knn):
+    if pts_tensor.numel() == 0:
+        print("Point cloud is empty!")
+        return None, None
+
+    pts_np = pts_tensor.detach().cpu().numpy() if pts_tensor.is_cuda else pts_tensor.numpy()
+    pts_np_cont = np.ascontiguousarray(pts_np, dtype=np.float64)
+
+    pcd = o3d.geometry.PointCloud()
+    pcd.points = o3d.utility.Vector3dVector(pts_np_cont)
+
+    pcd_tree = o3d.geometry.KDTreeFlann(pcd)
+    
+    indices = []
+    sq_dists = []
+
+    for p in pts_np_cont:
+        [_, idx, dist] = pcd_tree.search_knn_vector_3d(p, num_knn + 1)
+        indices.append(idx[1:])  # Skip the first index since it's the point itself
+        sq_dists.append(dist[1:])
+
+    return torch.tensor(sq_dists, dtype=pts_tensor.dtype, device=pts_tensor.device), torch.tensor(indices, dtype=torch.long, device=pts_tensor.device)
+
+
+def params2cpu(params, is_initial_timestep):
+    if is_initial_timestep:
+        res = {k: v.detach().cpu().contiguous().numpy() for k, v in params.items()}
+    else:
+        res = {k: v.detach().cpu().contiguous().numpy() for k, v in params.items() if
+               k in ['means3D', 'rgb_colors', 'unnorm_rotations']}
+    return res
+
+
+def save_params(output_params, seq, exp):
+    to_save = {}
+    for k in output_params[0].keys():
+        if k in output_params[1].keys():
+            to_save[k] = np.stack([params[k] for params in output_params])
+        else:
+            to_save[k] = output_params[0][k]
+    os.makedirs(f"./output/{exp}/{seq}", exist_ok=True)
+    np.savez(f"./output/{exp}/{seq}/params", **to_save)
+
+
+def farthest_point_sample(xyz, npoint):
+    """
+    Input:
+        xyz: pointcloud data, [B, N, C]
+        npoint: number of samples
+    Return:
+        centroids: sampled pointcloud index, [B, npoint]
+    """
+    device = xyz.device
+    B, N, C = xyz.shape
+    centroids = torch.zeros(B, npoint, dtype=torch.long).to(device)
+    distance = torch.ones(B, N).to(device) * 1e10
+    farthest = torch.randint(0, N, (B,), dtype=torch.long).to(device)
+    batch_indices = torch.arange(B, dtype=torch.long).to(device)
+    for i in range(npoint):
+        centroids[:, i] = farthest
+        centroid = xyz[batch_indices, farthest, :].view(B, 1, C)
+        dist = torch.sum((xyz - centroid) ** 2, -1)
+        mask = dist < distance
+        distance[mask] = dist[mask]
+        farthest = torch.max(distance, -1)[1]
+    return centroids
+
+def quat2mat(q):
+    norm = torch.sqrt(q[:, 0] * q[:, 0] + q[:, 1] * q[:, 1] + q[:, 2] * q[:, 2] + q[:, 3] * q[:, 3])
+    q = q / norm[:, None]
+    rot = torch.zeros((q.shape[0], 3, 3)).to(q.device)
+    r = q[:, 0]
+    x = q[:, 1]
+    y = q[:, 2]
+    z = q[:, 3]
+    rot[:, 0, 0] = 1 - 2 * (y * y + z * z)
+    rot[:, 0, 1] = 2 * (x * y - r * z)
+    rot[:, 0, 2] = 2 * (x * z + r * y)
+    rot[:, 1, 0] = 2 * (x * y + r * z)
+    rot[:, 1, 1] = 1 - 2 * (x * x + z * z)
+    rot[:, 1, 2] = 2 * (y * z - r * x)
+    rot[:, 2, 0] = 2 * (x * z - r * y)
+    rot[:, 2, 1] = 2 * (y * z + r * x)
+    rot[:, 2, 2] = 1 - 2 * (x * x + y * y)
+    return rot
+
+def rot2quat(rot):
+    # Preallocate quaternion tensor
+    q = torch.zeros((rot.shape[0], 4)).to(rot.device)
+    
+    # Compute quaternion components
+    q[:, 0] = 0.5 * torch.sqrt(1 + rot[:, 0, 0] + rot[:, 1, 1] + rot[:, 2, 2])
+    q[:, 1] = (rot[:, 2, 1] - rot[:, 1, 2]) / (4 * q[:, 0])
+    q[:, 2] = (rot[:, 0, 2] - rot[:, 2, 0]) / (4 * q[:, 0])
+    q[:, 3] = (rot[:, 1, 0] - rot[:, 0, 1]) / (4 * q[:, 0])
+    
+    return q
\ No newline at end of file
diff --git a/src/experiments/real_world/gs/sh_utils.py b/src/experiments/real_world/gs/sh_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..7de314dfb158b5bd46512856285e2d403107dc6f
--- /dev/null
+++ b/src/experiments/real_world/gs/sh_utils.py
@@ -0,0 +1,120 @@
+#  Copyright 2021 The PlenOctree Authors.
+#  Redistribution and use in source and binary forms, with or without
+#  modification, are permitted provided that the following conditions are met:
+#
+#  1. Redistributions of source code must retain the above copyright notice,
+#  this list of conditions and the following disclaimer.
+#
+#  2. Redistributions in binary form must reproduce the above copyright notice,
+#  this list of conditions and the following disclaimer in the documentation
+#  and/or other materials provided with the distribution.
+#
+#  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+#  AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+#  IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+#  ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
+#  LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
+#  CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
+#  SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
+#  INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
+#  CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
+#  ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
+#  POSSIBILITY OF SUCH DAMAGE.
+
+import torch
+
+C0 = 0.28209479177387814
+C1 = 0.4886025119029199
+C2 = [
+    1.0925484305920792,
+    -1.0925484305920792,
+    0.31539156525252005,
+    -1.0925484305920792,
+    0.5462742152960396
+]
+C3 = [
+    -0.5900435899266435,
+    2.890611442640554,
+    -0.4570457994644658,
+    0.3731763325901154,
+    -0.4570457994644658,
+    1.445305721320277,
+    -0.5900435899266435
+]
+C4 = [
+    2.5033429417967046,
+    -1.7701307697799304,
+    0.9461746957575601,
+    -0.6690465435572892,
+    0.10578554691520431,
+    -0.6690465435572892,
+    0.47308734787878004,
+    -1.7701307697799304,
+    0.6258357354491761,
+]
+
+
+def eval_sh(deg, sh, dirs):
+    """
+    Evaluate spherical harmonics at unit directions
+    using hardcoded SH polynomials.
+    Works with torch/np/jnp.
+    ... Can be 0 or more batch dimensions.
+    Args:
+        deg: int SH deg. Currently, 0-3 supported
+        sh: jnp.ndarray SH coeffs [..., C, (deg + 1) ** 2]
+        dirs: jnp.ndarray unit directions [..., 3]
+    Returns:
+        [..., C]
+    """
+    assert deg <= 4 and deg >= 0
+    coeff = (deg + 1) ** 2
+    assert sh.shape[-1] >= coeff
+
+    result = C0 * sh[..., 0]
+    if deg > 0:
+        x, y, z = dirs[..., 0:1], dirs[..., 1:2], dirs[..., 2:3]
+        result = (result -
+                  C1 * y * sh[..., 1] +
+                  C1 * z * sh[..., 2] -
+                  C1 * x * sh[..., 3])
+
+        if deg > 1:
+            xx, yy, zz = x * x, y * y, z * z
+            xy, yz, xz = x * y, y * z, x * z
+            result = (result +
+                      C2[0] * xy * sh[..., 4] +
+                      C2[1] * yz * sh[..., 5] +
+                      C2[2] * (2.0 * zz - xx - yy) * sh[..., 6] +
+                      C2[3] * xz * sh[..., 7] +
+                      C2[4] * (xx - yy) * sh[..., 8])
+
+            if deg > 2:
+                result = (result +
+                          C3[0] * y * (3 * xx - yy) * sh[..., 9] +
+                          C3[1] * xy * z * sh[..., 10] +
+                          C3[2] * y * (4 * zz - xx - yy) * sh[..., 11] +
+                          C3[3] * z * (2 * zz - 3 * xx - 3 * yy) * sh[..., 12] +
+                          C3[4] * x * (4 * zz - xx - yy) * sh[..., 13] +
+                          C3[5] * z * (xx - yy) * sh[..., 14] +
+                          C3[6] * x * (xx - 3 * yy) * sh[..., 15])
+
+                if deg > 3:
+                    result = (result + C4[0] * xy * (xx - yy) * sh[..., 16] +
+                              C4[1] * yz * (3 * xx - yy) * sh[..., 17] +
+                              C4[2] * xy * (7 * zz - 1) * sh[..., 18] +
+                              C4[3] * yz * (7 * zz - 3) * sh[..., 19] +
+                              C4[4] * (zz * (35 * zz - 30) + 3) * sh[..., 20] +
+                              C4[5] * xz * (7 * zz - 3) * sh[..., 21] +
+                              C4[6] * (xx - yy) * (7 * zz - 1) * sh[..., 22] +
+                              C4[7] * xz * (xx - 3 * yy) * sh[..., 23] +
+                              C4[8] * (xx * (xx - 3 * yy) - yy * (3 * xx - yy)) * sh[..., 24])
+    return result
+
+
+def RGB2SH(rgb):
+    return (rgb - 0.5) / C0
+
+
+def SH2RGB(sh):
+    return sh * C0 + 0.5
diff --git a/src/experiments/real_world/gs/train_utils.py b/src/experiments/real_world/gs/train_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..ffc390ba9663cd1cb816f7f4458e5e1197747808
--- /dev/null
+++ b/src/experiments/real_world/gs/train_utils.py
@@ -0,0 +1,174 @@
+from pathlib import Path
+import torch
+import numpy as np
+import random
+from PIL import Image
+
+from pgnd.utils import get_root
+import sys
+root: Path = get_root(__file__)
+
+from diff_gaussian_rasterization import GaussianRasterizer as Renderer
+from gs.helpers import setup_camera, l1_loss_v1, o3d_knn, params2rendervar
+from gs.external import calc_ssim, calc_psnr
+
+
+def get_custom_dataset(img_list, seg_list, metadata):
+    """
+    Generates a dataset from given metadata and sequence.
+    """
+    dataset = []
+    
+    # Loop over filenames corresponding to 't' in the metadata
+    for c in range(len(img_list)):
+        
+        # Extract parameters from the metadata
+        w, h = metadata['w'], metadata['h']
+        k = metadata['k'][c]
+        w2c = metadata['w2c'][c]
+        
+        # Set up a camera using extracted parameters and some default values
+        cam = setup_camera(w, h, k, w2c, near=0.01, far=100)
+        
+        # Get the filename of the current image and open it
+        if isinstance(img_list[c], str):
+            im = np.array(Image.open(img_list[c]))
+            im = torch.tensor(im).float().cuda().permute(2, 0, 1) / 255
+        else:
+            im = torch.tensor(img_list[c]).permute(2, 0, 1).float().cuda()
+            if im.max() > 2.0: 
+                im = im / 255
+        
+        # Open the corresponding segmentation image and convert it to a tensor
+        if isinstance(seg_list[c], str):
+            seg = np.array(Image.open(seg_list[c])).astype(np.float32)
+        else:
+            seg = seg_list[c].astype(np.float32)
+        seg = torch.tensor(seg).float().cuda()
+
+        # Create a color segmentation tensor. It seems to treat the segmentation as binary (object/background)
+        seg_col = torch.stack((seg, torch.zeros_like(seg), 1 - seg))
+        
+        # Add the data to the dataset
+        dataset.append({'cam': cam, 'im': im, 'seg': seg_col, 'id': c})
+
+    return dataset
+
+
+def initialize_params(init_pt_cld, metadata):
+    """
+    Initializes parameters and variables required for a 3D point cloud based on provided data.
+    """
+
+    # Extract the segmentation data.
+    seg = init_pt_cld[:, 6]
+
+    # Define a constant for the maximum number of cameras.
+    max_cams = 4
+
+    # Compute the squared distance for the K-Nearest Neighbors of each point in the 3D point cloud.
+    sq_dist, indices = o3d_knn(init_pt_cld[:, :3], 3)
+
+    # Calculate the mean squared distance for the 3 closest points and clip its minimum value.
+    mean3_sq_dist = sq_dist.mean(-1).clip(min=0.0000001)
+
+    # Initialize various parameters related to the 3D point cloud.
+    params = {
+        'means3D': init_pt_cld[:, :3],                          # 3D coordinates of the points.
+        'rgb_colors': init_pt_cld[:, 3:6],                      # RGB color values for the points.
+        'seg_colors': np.stack((seg, np.zeros_like(seg), 1 - seg), -1),  # Segmentation colors.
+        'unnorm_rotations': np.tile([1, 0, 0, 0], (seg.shape[0], 1)),  # Default rotations for each point.
+        'logit_opacities': np.zeros((seg.shape[0], 1)),         # Initial opacity values for the points.
+        'log_scales': np.tile(np.log(np.sqrt(mean3_sq_dist))[..., None], (1, 3)),  # Scale factors for the points.
+        'cam_m': np.zeros((max_cams, 3)),                       # Placeholder for camera motion.
+        'cam_c': np.zeros((max_cams, 3)),                       # Placeholder for camera center.
+    }
+
+    # Convert the params to PyTorch tensors and move them to the GPU.
+    params = {k: torch.nn.Parameter(torch.tensor(v).cuda().float().contiguous().requires_grad_(True)) for k, v in params.items()}
+    # params['rgb_colors'].requires_grad = False
+
+    # Calculate the scene radius based on the camera centers.
+    cam_centers = np.linalg.inv(metadata['w2c'])[:, :3, 3]
+    scene_radius = 1.1 * np.max(np.linalg.norm(cam_centers - np.mean(cam_centers, 0)[None], axis=-1))
+
+    # Initialize other associated variables.
+    variables = {
+        'max_2D_radius': torch.zeros(params['means3D'].shape[0]).cuda().float(), # Maximum 2D radius.
+        'scene_radius': scene_radius,                                          # Scene radius.
+        'means2D_gradient_accum': torch.zeros(params['means3D'].shape[0]).cuda().float(), # Means2D gradient accumulator.
+        'denom': torch.zeros(params['means3D'].shape[0]).cuda().float()        # Denominator.
+    }
+
+    return params, variables
+
+
+def initialize_optimizer(params, variables):
+    lrs = {
+        'means3D': 0.00016 * variables['scene_radius'],
+        'rgb_colors': 0.0,
+        'seg_colors': 0.0,
+        'unnorm_rotations': 0.001,
+        'logit_opacities': 0.05,
+        'log_scales': 0.001,
+        'cam_m': 1e-4,
+        'cam_c': 1e-4,
+    }
+    param_groups = [{'params': [v], 'name': k, 'lr': lrs[k]} for k, v in params.items()]
+    return torch.optim.Adam(param_groups, lr=0.0, eps=1e-15)
+
+
+def get_loss(params, curr_data, variables, loss_weights):
+
+    # Initialize dictionary to store various loss components
+    losses = {}
+
+    # Convert parameters to rendering variables and retain gradient for 'means2D'
+    rendervar = params2rendervar(params)
+    rendervar['means2D'].retain_grad()
+
+    # Perform rendering to obtain image, radius, and other outputs
+    im, radius, _ = Renderer(raster_settings=curr_data['cam'])(**rendervar)
+
+    # Apply camera parameters to modify the rendered image
+    curr_id = curr_data['id']
+    im = torch.exp(params['cam_m'][curr_id])[:, None, None] * im + params['cam_c'][curr_id][:, None, None]
+
+    # Calculate image loss using L1 loss and ssim
+    losses['im'] = 0.8 * l1_loss_v1(im, curr_data['im']) + 0.2 * (1.0 - calc_ssim(im, curr_data['im']))
+    variables['means2D'] = rendervar['means2D']  # Gradient only accum from colour render for densification
+
+    segrendervar = params2rendervar(params)
+    segrendervar['colors_precomp'] = params['seg_colors']
+    seg, _, _, = Renderer(raster_settings=curr_data['cam'])(**segrendervar)
+
+    # Calculate segmentation loss
+    losses['seg'] = 0.8 * l1_loss_v1(seg, curr_data['seg']) + 0.2 * (1.0 - calc_ssim(seg, curr_data['seg']))
+
+    # Calculate total loss as weighted sum of individual losses
+    loss = sum([loss_weights[k] * v for k, v in losses.items()])
+
+    # Update variables related to rendering radius and seen areas
+    seen = radius > 0
+    variables['max_2D_radius'][seen] = torch.max(radius[seen], variables['max_2D_radius'][seen])
+    variables['seen'] = seen
+    return loss, variables
+
+
+def report_progress(params, data, i, progress_bar, num_pts, every_i=100, vis_dir=None):
+    if i % every_i == 0:
+        im, _, _, = Renderer(raster_settings=data['cam'])(**params2rendervar(params))
+        curr_id = data['id']
+        im = torch.exp(params['cam_m'][curr_id])[:, None, None] * im + params['cam_c'][curr_id][:, None, None]
+        if vis_dir:
+            Image.fromarray((im.cpu().numpy().clip(0, 1) * 255).astype(np.uint8).transpose(1, 2, 0)).save(f"{vis_dir}/{i:06d}.png")
+        psnr = calc_psnr(im, data['im']).mean()
+        progress_bar.set_postfix({"img 0 PSNR": f"{psnr:.{7}f}, number of points: {num_pts}"})
+        progress_bar.update(every_i)
+
+
+def get_batch(todo_dataset, dataset):
+    if not todo_dataset:
+        todo_dataset = dataset.copy()
+    curr_data = todo_dataset.pop(random.randint(0, len(todo_dataset) - 1))
+    return curr_data
diff --git a/src/experiments/real_world/gs/trainer.py b/src/experiments/real_world/gs/trainer.py
new file mode 100644
index 0000000000000000000000000000000000000000..de24d5308d1cf93cae3054b2d46e85314bcf8de8
--- /dev/null
+++ b/src/experiments/real_world/gs/trainer.py
@@ -0,0 +1,215 @@
+from pathlib import Path
+import torch
+import numpy as np
+import cv2
+import os
+import subprocess
+import open3d as o3d
+from tqdm import tqdm
+
+from pgnd.utils import get_root
+from pgnd.ffmpeg import make_video
+import sys
+root: Path = get_root(__file__)
+
+from diff_gaussian_rasterization import GaussianRasterizer
+from diff_gaussian_rasterization import GaussianRasterizationSettings as Camera
+from gs.helpers import setup_camera
+from gs.external import densify
+from gs.train_utils import get_custom_dataset, initialize_params, initialize_optimizer, get_loss, report_progress, get_batch
+
+
+def Rt_to_w2c(R, t):
+    w2c = np.concatenate([np.concatenate([R, t.reshape(3, 1)], axis=1), np.array([[0, 0, 0, 1]])], axis=0)
+    w2c = np.linalg.inv(w2c)
+    return w2c
+
+
+class GSTrainer:
+
+    def __init__(self, config, device):
+        self.config = config
+        self.device = device
+        self.clear()
+    
+    def clear(self, clear_params=True):
+        # training data
+        self.init_pt_cld = None
+        self.metadata = None
+        self.img_list = None
+        self.seg_list = None
+        # training results
+        if clear_params:
+            self.params = None
+    
+    @torch.no_grad
+    def render(self, render_data, cam_id, bg=[0.7, 0.7, 0.7]):
+        render_data = {k: v.to(self.device) for k, v in render_data.items()}
+        w, h = self.metadata['w'], self.metadata['h']
+        k = self.metadata['k'][cam_id]
+        w2c = self.metadata['w2c'][cam_id]
+        cam = setup_camera(w, h, k, w2c, self.config['near'], self.config['far'], bg)
+        im, _, depth, = GaussianRasterizer(raster_settings=cam)(**render_data)
+        return im, depth
+    
+    def update_state_env(self, pcd, env, imgs, masks):  # RGB
+        R_list, t_list = env.get_extrinsics()
+        intr_list = env.get_intrinsics()
+        rgb_list = []
+        seg_list = []
+        for c in range(env.n_fixed_cameras):
+            rgb_list.append(imgs[c] * masks[c][:, :, None])
+            seg_list.append(masks[c] * 1.0)
+        self.update_state(pcd, rgb_list, seg_list, R_list, t_list, intr_list)
+
+    def update_state_no_env(self, pcd, imgs, masks, R_list, t_list, intr_list, n_cameras=4):  # RGB
+        rgb_list = []
+        seg_list = []
+        for c in range(n_cameras):
+            rgb_list.append(imgs[c] * masks[c][:, :, None])
+            seg_list.append(masks[c] * 1.0)
+        self.update_state(pcd, rgb_list, seg_list, R_list, t_list, intr_list)
+
+    def update_state(self, pcd, img_list, seg_list, R_list, t_list, intr_list):
+        pts = np.array(pcd.points).astype(np.float32)
+        colors = np.array(pcd.colors).astype(np.float32)
+        seg = np.ones_like(pts[:, 0:1])
+        self.init_pt_cld = np.concatenate([pts, colors, seg], axis=1)
+        w, h = img_list[0].shape[1], img_list[0].shape[0]
+        assert np.all([img.shape[1] == w and img.shape[0] == h for img in img_list])
+        self.metadata = {
+            'w': w,
+            'h': h,
+            'k': [intr for intr in intr_list],
+            'w2c': [Rt_to_w2c(R, t) for R, t in zip(R_list, t_list)]
+        }
+        self.img_list = img_list
+        self.seg_list = seg_list
+    
+    def train(self, vis_dir):
+        params, variables = initialize_params(self.init_pt_cld, self.metadata)
+        optimizer = initialize_optimizer(params, variables)
+        dataset = get_custom_dataset(self.img_list, self.seg_list, self.metadata)
+        todo_dataset = []
+        num_iters = self.config['num_iters']
+        loss_weights = {'im': self.config['weight_im'], 'seg': self.config['weight_seg']}
+        densify_params = {
+            'grad_thresh': self.config['grad_thresh'],
+            'remove_thresh': self.config['remove_threshold'], 
+            'remove_thresh_5k': self.config['remove_thresh_5k'], 
+            'scale_scene_radius': self.config['scale_scene_radius']
+        }
+        progress_bar = tqdm(range(num_iters), dynamic_ncols=True)
+        for i in range(num_iters): 
+            curr_data = get_batch(todo_dataset, dataset)
+            loss, variables = get_loss(params, curr_data, variables, loss_weights)
+            loss.backward()
+            with torch.no_grad():
+                params, variables, num_pts = densify(params, variables, optimizer, i, **densify_params)
+                report_progress(params, dataset[0], i, progress_bar, num_pts, vis_dir=vis_dir)
+                optimizer.step()
+                optimizer.zero_grad(set_to_none=True)
+        progress_bar.close()
+        params = {k: v.detach() for k, v in params.items()}
+        self.params = params
+
+    def rollout_and_render(self, dm, action, vis_dir=None, save_images=True, overwrite_params=True, remove_black=False):
+        assert vis_dir is not None
+        assert self.params is not None
+
+        xyz_0 = self.params['means3D']
+        rgb_0 = self.params['rgb_colors']
+        quat_0 = torch.nn.functional.normalize(self.params['unnorm_rotations'])
+        opa_0 = torch.sigmoid(self.params['logit_opacities'])
+        scales_0 = torch.exp(self.params['log_scales'])
+
+        low_opa_idx = opa_0[:, 0] < 0.1
+        xyz_0 = xyz_0[~low_opa_idx]
+        rgb_0 = rgb_0[~low_opa_idx]
+        quat_0 = quat_0[~low_opa_idx]
+        opa_0 = opa_0[~low_opa_idx]
+        scales_0 = scales_0[~low_opa_idx]
+
+        if remove_black:
+            low_color_idx = rgb_0.sum(dim=-1) < 0.5
+            xyz_0 = xyz_0[~low_color_idx]
+            rgb_0 = rgb_0[~low_color_idx]
+            quat_0 = quat_0[~low_color_idx]
+            opa_0 = opa_0[~low_color_idx]
+            scales_0 = scales_0[~low_color_idx]
+
+        eef_xyz_start = action[0]
+        eef_xyz_end = action[1]
+
+        dist_thresh = 0.005  # 5mm
+        n_steps = int((eef_xyz_end - eef_xyz_start).norm().item() / dist_thresh)
+        eef_xyz = torch.lerp(eef_xyz_start, eef_xyz_end, torch.linspace(0, 1, n_steps).to(self.device)[:, None])
+        eef_xyz_pad = torch.cat([eef_xyz, eef_xyz_end[None].repeat(dm.n_his, 1)], dim=0)
+        eef_xyz_pad = eef_xyz_pad[:, None]  # (n_steps, 1, 3)
+        n_steps = eef_xyz_pad.shape[0]
+
+        inlier_idx_all = np.arange(len(xyz_0))  # no outlier removal
+
+        xyz, rgb, quat, opa, xyz_bones, eef = dm.rollout(
+                xyz_0, rgb_0, quat_0, opa_0, eef_xyz_pad, n_steps, inlier_idx_all)
+
+        # interpolate smoothly
+        change_points = (xyz - torch.concatenate([xyz[0:1], xyz[:-1]], dim=0)).norm(dim=-1).sum(dim=-1).nonzero().squeeze(1)
+        change_points = torch.cat([torch.tensor([0]), change_points])
+        for i in range(1, len(change_points)):
+            start = change_points[i - 1]
+            end = change_points[i]
+            if end - start < 2:  # 0 or 1
+                continue
+            xyz[start:end] = torch.lerp(xyz[start][None], xyz[end][None], torch.linspace(0, 1, end - start + 1).to(xyz.device)[:, None, None])[:-1]
+            rgb[start:end] = torch.lerp(rgb[start][None], rgb[end][None], torch.linspace(0, 1, end - start + 1).to(rgb.device)[:, None, None])[:-1]
+            quat[start:end] = torch.lerp(quat[start][None], quat[end][None], torch.linspace(0, 1, end - start + 1).to(quat.device)[:, None, None])[:-1]
+            opa[start:end] = torch.lerp(opa[start][None], opa[end][None], torch.linspace(0, 1, end - start + 1).to(opa.device)[:, None, None])[:-1]
+            xyz_bones[start:end] = torch.lerp(xyz_bones[start][None], xyz_bones[end][None], torch.linspace(0, 1, end - start + 1).to(xyz_bones.device)[:, None, None])[:-1]
+            eef[start:end] = torch.lerp(eef[start][None], eef[end][None], torch.linspace(0, 1, end - start + 1).to(eef.device)[:, None, None])[:-1]
+        
+        for _ in range(3):
+            xyz[1:-1] = (xyz[:-2] + 2 * xyz[1:-1] + xyz[2:]) / 4
+            # quat[1:-1] = (quat[:-2] + 2 * quat[1:-1] + quat[2:]) / 4
+
+        quat = torch.nn.functional.normalize(quat, dim=-1)
+
+        rendervar_list = []
+        visvar_list = []
+        # im_list = []
+        for t in range(n_steps):
+            rendervar = {
+                'means3D': xyz[t],  
+                'colors_precomp': rgb[t],
+                'rotations': quat[t],
+                'opacities': opa[t],
+                'scales': scales_0,
+                'means2D': torch.zeros_like(xyz[t]),
+            }
+            rendervar_list.append(rendervar)
+
+            visvar = {
+                'xyz_bones': xyz_bones[t].numpy(), # params['means3D'][t][fps_idx].detach().cpu().numpy(),
+                'eef': eef[t].numpy(), # eef_xyz[t].detach().cpu().numpy(),
+            }
+            visvar_list.append(visvar)
+            
+            if save_images:
+                im, _ = self.render(rendervar, 0, bg=[0, 0, 0])
+                im = im.cpu().numpy().transpose(1, 2, 0)
+                im = (im * 255).astype(np.uint8)
+                # im_list.append(im)
+                cv2.imwrite(os.path.join(vis_dir, f'{t:04d}.png'), im[:, :, ::-1].copy())
+        
+        if save_images:
+            make_video(vis_dir, os.path.join(os.path.dirname(vis_dir), f"{vis_dir.split('/')[-1]}.mp4"), '%04d.png', 5)
+        
+        if overwrite_params:
+            self.params['means3D'] = xyz[-1].to(self.device)
+            self.params['rgb_colors'] = rgb[-1].to(self.device)
+            self.params['unnorm_rotations'] = quat[-1].to(self.device)
+            self.params['logit_opacities'] = torch.logit(opa[-1]).to(self.device)
+            self.params['log_scales'] = torch.log(scales_0).to(self.device)
+            self.params['means2D'] = torch.zeros_like(xyz[-1]).to(self.device)
+
+        return rendervar_list, visvar_list
diff --git a/src/experiments/real_world/modules_planning/__init__.py b/src/experiments/real_world/modules_planning/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/src/experiments/real_world/modules_planning/common/communication.py b/src/experiments/real_world/modules_planning/common/communication.py
new file mode 100644
index 0000000000000000000000000000000000000000..b5bf90fc2c8b415caeff9754f1351d94081ed140
--- /dev/null
+++ b/src/experiments/real_world/modules_planning/common/communication.py
@@ -0,0 +1,6 @@
+XARM_STATE_PORT = 10101
+XARM_CONTROL_PORT = 10102
+OBJECT_POSE_PORT = 10109
+
+XARM_CONTROL_PORT_L = 10102
+XARM_CONTROL_PORT_R = 10110
diff --git a/src/experiments/real_world/modules_planning/common/xarm.py b/src/experiments/real_world/modules_planning/common/xarm.py
new file mode 100644
index 0000000000000000000000000000000000000000..f2321caf3e006afaf49628abe8ce1fbe507eb03f
--- /dev/null
+++ b/src/experiments/real_world/modules_planning/common/xarm.py
@@ -0,0 +1,15 @@
+XY_MIN, XY_MAX = 50, 800
+X_MIN,X_MAX = 0, 800
+Y_MIN,Y_MAX = -350, 350
+Z_MIN, Z_MAX = 110, 650
+
+MOVE_RATE = 1000  # hz; macro velocity = move_rate * xyz_velocity 
+MOVE_SLEEP = 1 / MOVE_RATE
+XYZ_VELOCITY = 1.0  # mm (in every MOVE_SLEEP interval)
+ANGLE_VELOCITY_MAX = 0.05  # degree
+
+GRIPPER_OPEN_MAX = 800
+GRIPPER_OPEN_MIN = 0
+
+POSITION_UPDATE_INTERVAL = 0.02  # 100hz the reader must read faster than this
+COMMAND_CHECK_INTERVAL = 0.02  # 100hz command must be sent faster than this
\ No newline at end of file
diff --git a/src/experiments/real_world/modules_planning/dynamics_module.py b/src/experiments/real_world/modules_planning/dynamics_module.py
new file mode 100644
index 0000000000000000000000000000000000000000..2330dda49a17530fb763afe0f464bb017f3000a3
--- /dev/null
+++ b/src/experiments/real_world/modules_planning/dynamics_module.py
@@ -0,0 +1,373 @@
+import numpy as np
+from pathlib import Path
+from copy import deepcopy
+from functools import partial
+import torch
+import torch.nn as nn
+import warp as wp
+from dgl.geometry import farthest_point_sampler
+import random
+import kornia
+import open3d as o3d
+from tqdm import tqdm, trange
+
+from pgnd.sim import Friction, CacheDiffSimWithFrictionBatch, MPMStaticsBatch, MPMCollidersBatch
+from pgnd.utils import get_root, mkdir
+from pgnd.ffmpeg import make_video
+
+from train_eval import transform_gripper_points
+from gs.convert import read_splat
+
+root: Path = get_root(__file__)
+
+
+def fps(x, n, device, random_start=False):
+    start_idx = random.randint(0, x.shape[0] - 1) if random_start else 0
+    fps_idx = farthest_point_sampler(x[None], n, start_idx=start_idx)[0]
+    fps_idx = fps_idx.to(x.device)
+    return fps_idx
+
+
+class DynamicsModule:
+
+    def __init__(self, cfg, exp_root, ckpt_path, batch_size, num_steps_total):
+
+        wp.init()
+        wp.ScopedTimer.enabled = False
+        wp.set_module_options({'fast_math': False})
+        wp.config.verify_autograd_array_access = True
+
+        self.exp_root = exp_root
+        self.batch_size = batch_size
+        self.num_steps_total = num_steps_total
+
+        gpus = [int(gpu) for gpu in cfg.gpus]
+        self.gpus = gpus
+
+        wp_devices = [wp.get_device(f'cuda:{gpu}') for gpu in gpus]
+        torch_devices = [torch.device(f'cuda:{gpu}') for gpu in gpus]
+        device_count = len(torch_devices)
+        assert device_count == 1
+        wp_device = wp_devices[0]
+        torch_device = torch_devices[0]
+        self.device = torch_device
+        
+        self.cfg = cfg
+        n_history = cfg.sim.n_history
+        material: nn.Module = getattr(pgnd.material, cfg.env.blob.material.material.cls)(cfg.env.blob.material.material, n_history)
+        material.set_params(num_grids=cfg.sim.num_grids)
+        material.to(torch_device)
+        material.requires_grad_(False)
+        material.train(False)
+
+        friction: nn.Module = Friction(np.array([cfg.sim.friction])[None].repeat(batch_size, axis=0))  # type: ignore
+        friction.to(torch_device)
+        assert len(list(friction.parameters())) > 0
+        friction.requires_grad_(False)
+        friction.train(False)
+
+        ckpt = torch.load(ckpt_path, map_location=torch_device)
+        material.load_state_dict(ckpt['material'])
+        self.material = material
+        self.friction = friction
+
+        self.material.eval()
+        self.friction.eval()
+
+        self.preprocess_metadata = {}
+        self.downsample_indices = None
+        self.target_state = None
+
+        if cfg.sim.gripper_points:  # do some manual transformation here
+            pts, colors, scales, quats, opacities = read_splat('experiments/log/gs/ckpts/gripper_new.splat')
+            n_gripper_particles = 500
+            R = np.array(
+                [[1, 0, 0],
+                [0, 0, -1],
+                [0, 1, 0]]
+            )
+            eef_global_T = np.array([cfg.env.blob.eef_t[0], cfg.env.blob.eef_t[1], cfg.env.blob.eef_t[2] - 0.01])  # 1018_sloth: 0.185, 1018_rope_short: 0.013)
+            pts = pts + eef_global_T
+            pts = pts @ R.T
+            scale = cfg.sim.preprocess_scale
+            pts = pts * scale
+
+            axis = np.array([0, 1, 0])
+            angle = -27  # degree
+            R = o3d.geometry.get_rotation_matrix_from_axis_angle(axis * np.pi / 180 * angle)
+            translation = np.array([-0.015, 0.06, 0])
+
+            pts = pts @ R.T
+            pts = pts + translation
+
+            R = np.array(
+                [[0, 0, 1],
+                [-1, 0, 0],
+                [0, -1, 0]]
+            )
+            pts = pts @ R.T
+
+            gripper_pts = torch.from_numpy(pts).to(torch.float32).to(self.device)
+            downsample_indices = fps(gripper_pts, n_gripper_particles, self.device, random_start=True)
+            gripper_pts = gripper_pts[downsample_indices]
+            self.gripper_pts = gripper_pts
+
+    def set_target_state(self, target_state):
+        self.target_state = target_state
+
+    def reset_model(self, x=None):
+        return
+    
+    def reset_preprocess_meta(self, pts):
+        cfg = self.cfg
+        dx = cfg.sim.num_grids[-1]
+
+        p_x = torch.tensor(pts).to(torch.float32).to(self.device)
+        R = torch.tensor(
+            [[1, 0, 0],
+            [0, 0, -1],
+            [0, 1, 0]]
+        ).to(p_x.device).to(p_x.dtype)
+        p_x_rotated = p_x @ R.T
+
+        scale = cfg.sim.preprocess_scale
+        p_x_rotated_scaled = p_x_rotated * scale
+
+        cfg = self.cfg
+        if cfg.sim.preprocess_with_table:
+            global_translation = torch.tensor([
+                0.5 - (p_x_rotated_scaled[:, 0].max() + p_x_rotated_scaled[:, 0].min()) / 2,
+                dx * (cfg.env.blob.clip_bound + 0.5) + 1e-5 - p_x_rotated_scaled[:, 1].min(),
+                0.5 - (p_x_rotated_scaled[:, 2].max() + p_x_rotated_scaled[:, 2].min()) / 2,
+            ], dtype=p_x_rotated_scaled.dtype, device=p_x_rotated_scaled.device)
+        else:
+            global_translation = torch.tensor([
+                0.5 - (p_x_rotated_scaled[:, 0].max() + p_x_rotated_scaled[:, 0].min()) / 2,
+                0.5 - (p_x_rotated_scaled[:, 1].max() + p_x_rotated_scaled[:, 1].min()) / 2,
+                0.5 - (p_x_rotated_scaled[:, 2].max() + p_x_rotated_scaled[:, 2].min()) / 2,
+            ], dtype=p_x_rotated_scaled.dtype, device=p_x_rotated_scaled.device)
+
+        self.preprocess_metadata = {
+            'R': R,
+            'scale': scale,
+            'global_translation': global_translation,
+        }
+    
+    def reset_downsample_indices(self, pts, uniform=True):
+        cfg = self.cfg
+        if uniform:
+            downsample_indices = fps(pts, cfg.sim.n_particles, self.device, random_start=True)
+        else:
+            downsample_indices = torch.randperm(pts.shape[0])[:cfg.sim.n_particles]
+        self.downsample_indices = downsample_indices
+
+    def rollout(self, pts, eef_xyz, eef_rot, eef_gripper, pts_his=None):
+        cfg = self.cfg
+
+        # preprocess eef
+        # eef_xyz: (batch_size, n_look_forward + 1, n_grippers, 3)
+        # eef_rot: (batch_size, n_look_forward + 1, n_grippers, 3, 3)
+        # eef_gripper: (batch_size, n_look_forward + 1, n_grippers, 1)  0: close, 1: open
+        batch_size = eef_xyz.shape[0]
+        assert eef_xyz.shape[1] == eef_rot.shape[1] == eef_gripper.shape[1]
+
+        R = self.preprocess_metadata['R']
+        scale = self.preprocess_metadata['scale']
+        global_translation = self.preprocess_metadata['global_translation']
+
+        eef_xyz = eef_xyz @ R.T
+        eef_xyz = eef_xyz * scale
+        eef_xyz += global_translation
+
+        eef_rot = eef_rot @ R.T
+        eef_quat = kornia.geometry.conversions.rotation_matrix_to_quaternion(eef_rot)
+
+        n_frames = eef_xyz.shape[1] - 1
+        eef_vel = torch.zeros_like(eef_xyz[:, 1:])  # (batch_size, n_frames, n_grippers, 3)
+        eef_vel = (eef_xyz[:, 1:] - eef_xyz[:, :-1]) / cfg.sim.dt
+
+        eef_rot_this = kornia.geometry.conversions.quaternion_to_rotation_matrix(eef_quat[:, :-1].reshape(-1, 4))  # (batch_size * n_frames * n_grippers, 3, 3)
+        eef_rot_next = kornia.geometry.conversions.quaternion_to_rotation_matrix(eef_quat[:, 1:].reshape(-1, 4))  # (batch_size * n_frames * n_grippers, 3, 3)
+        eef_rot_delta = eef_rot_this.bmm(eef_rot_next.inverse())
+        eef_aa = kornia.geometry.conversions.rotation_matrix_to_axis_angle(eef_rot_delta)  # (batch_size * n_frames * n_grippers, 3)
+
+        eef_quat_vel = torch.zeros((batch_size, n_frames, cfg.sim.num_grippers, 3)).to(self.device).to(torch.float32)
+        eef_quat_vel = eef_aa.reshape(batch_size, n_frames, -1, 3) / cfg.sim.dt  # (batch_size, n_frames, n_grippers, 3), radian per second
+
+        grippers = torch.zeros((batch_size, n_frames, cfg.sim.num_grippers, 15)).to(self.device).to(torch.float32)
+        grippers[:, :, :, :3] = eef_xyz[:, :-1]  # not using the last position although we gonna arrive there
+        grippers[:, :, :, 3:6] = eef_vel
+        grippers[:, :, :, 6:10] = eef_quat[:, :-1]
+        grippers[:, :, :, 10:13] = eef_quat_vel
+        grippers[:, :, :, 13] = cfg.env.blob.gripper_radius
+        grippers[:, :, :, 14] = eef_gripper[:, :-1].squeeze(-1)
+
+        # preprocess pts
+        x = pts[self.downsample_indices]
+
+        R = self.preprocess_metadata['R']
+        scale = self.preprocess_metadata['scale']
+        global_translation = self.preprocess_metadata['global_translation']
+
+        # data frame to model frame
+        x = x @ R.T
+        x = x * scale
+        x = x + global_translation
+
+        x = x[None].repeat(batch_size, 1, 1)
+        x_pred, v_pred = self.rollout_preprocessed(x, grippers=grippers)  # assumes static
+        
+        # inverse preprocess
+        x_pred = x_pred - global_translation
+        x_pred = x_pred / scale
+        x_pred = x_pred @ R
+
+        v_pred = v_pred / scale
+        v_pred = v_pred @ R
+        return x_pred, v_pred
+
+    @torch.no_grad()
+    def rollout_preprocessed(self, x, v=None, x_his=None, v_his=None, grippers=None):
+        cfg = self.cfg
+
+        # reset model
+        wp_devices = [wp.get_device(f'cuda:{gpu}') for gpu in self.gpus]
+        torch_devices = [torch.device(f'cuda:{gpu}') for gpu in self.gpus]
+        device_count = len(torch_devices)
+        assert device_count == 1
+        wp_device = wp_devices[0]
+        torch_device = torch_devices[0]
+
+        batch_size = x.shape[0]
+        num_particles = x.shape[1]
+        assert num_particles == cfg.sim.n_particles
+        assert batch_size == self.batch_size
+
+        clip_bound = torch.tensor(cfg.env.blob.clip_bound)
+
+        if cfg.sim.gripper_points:
+            gripper_points = self.gripper_pts.clone()[None].repeat(batch_size, 1, 1)  # (bsz, num_grippers, 3)
+            gripper_x, gripper_v, gripper_mask = transform_gripper_points(cfg, gripper_points, grippers)  # (bsz, num_steps, num_grippers, 3)
+            num_gripper_particles = gripper_x.shape[2]
+            num_particles_orig = num_particles
+            num_particles = num_particles + num_gripper_particles
+            num_grippers = 0
+        else:
+            gripper_x = None
+            gripper_v = None
+            gripper_mask = None
+            num_particles_orig = num_particles
+            num_gripper_particles = 0
+            num_grippers = cfg.sim.num_grippers
+
+        sim = CacheDiffSimWithFrictionBatch(cfg, num_steps_total, batch_size, self.wp_device, requires_grad=True)
+
+        statics = StaticsBatch()
+        statics.init(shape=(batch_size, num_particles), device=self.wp_device)
+        statics.update_clip_bound(clip_bound)
+        colliders = CollidersBatch()
+        colliders.init(shape=(batch_size, num_grippers), device=wp_device)
+
+        if v is None:
+            v = torch.zeros_like(x)
+
+        if cfg.sim.n_history > 0:
+            if x_his is None:
+                x_his = x.clone().repeat(1, 1, cfg.sim.n_history)
+            if v_his is None:
+                v_his = v.clone().repeat(1, 1, cfg.sim.n_history)
+
+        colliders.initialize_grippers(grippers[:, 0])
+
+        assert cfg.sim.skip_frame == cfg.sim.interval  # required for the following line
+
+        xs = []
+        vs = []
+        for step in trange(self.num_steps_total):
+            colliders.update_grippers(grippers[:, step])  # ignore gripper radius
+            if cfg.sim.gripper_forcing:
+                x_in = x.clone()
+            else:
+                x_in = None
+
+            if cfg.sim.gripper_points:
+                assert gripper_x is not None and gripper_v is not None and gripper_mask is not None
+                x = torch.cat([x, gripper_x[:, step]], dim=1)  # gripper_x: (bsz, num_steps, num_particles, 3)
+                v = torch.cat([v, gripper_v[:, step]], dim=1)
+                x_his = torch.cat([x_his, torch.zeros((gripper_x.shape[0], gripper_x.shape[2], cfg.sim.n_history * 3), device=x_his.device, dtype=x_his.dtype)], dim=1)  # type: ignore
+                v_his = torch.cat([v_his, torch.zeros((gripper_x.shape[0], gripper_x.shape[2], cfg.sim.n_history * 3), device=v_his.device, dtype=v_his.dtype)], dim=1)  # type: ignore
+                
+                if C.shape[1] < num_particles:
+                    C = torch.cat([C, torch.zeros((gripper_x.shape[0], gripper_x.shape[2], 3, 3), device=C.device, dtype=C.dtype)], dim=1)
+                if F.shape[1] < num_particles:
+                    F = torch.cat([F, torch.eye(3, device=F.device).unsqueeze(0).unsqueeze(0).repeat(gripper_x.shape[0], gripper_x.shape[2], 1, 1)], dim=1)
+                if enabled.shape[1] < num_particles:
+                    enabled = torch.cat([enabled, gripper_mask[:, step]], dim=1)
+                statics.update_enabled(enabled.cpu())
+
+            pred = self.material(x, v, x_his, v_his, C, F)
+
+            if pred.isnan().any():
+                print('pred isnan', pred.min().item(), pred.max().item())
+                break
+            if pred.isinf().any():
+                print('pred isinf', pred.min().item(), pred.max().item())
+                break
+
+            x, v = sim(statics, colliders, step, x, v, self.friction.mu, pred)
+
+            if cfg.sim.gripper_forcing:
+                assert not cfg.sim.gripper_points
+                assert grippers is not None and x_in is not None
+                gripper_xyz = grippers[:, step, :, :3]  # (bsz, num_grippers, 3)
+                gripper_v = grippers[:, step, :, 3:6]  # (bsz, num_grippers, 3)
+                x_from_gripper = x_in[:, None] - gripper_xyz[:, :, None]  # (bsz, num_grippers, num_particles, 3)
+                x_gripper_distance = torch.norm(x_from_gripper, dim=-1)  # (bsz, num_grippers, num_particles)
+                x_gripper_distance_mask = x_gripper_distance < cfg.env.blob.gripper_radius
+                x_gripper_distance_mask = x_gripper_distance_mask.unsqueeze(-1).repeat(1, 1, 1, 3)  # (bsz, num_particles, num_grippers, 3)
+                gripper_v_expand = gripper_v[:, :, None].repeat(1, 1, num_particles, 1)  # (bsz, num_grippers, num_particles, 3)
+
+                gripper_closed = grippers[:, step, :, -1] < 0.5  # (bsz, num_grippers)  # 1: open, 0: close
+                x_gripper_distance_mask = torch.logical_and(x_gripper_distance_mask, gripper_closed[:, :, None, None].repeat(1, 1, num_particles, 3))
+
+                gripper_quat_vel = grippers[:, step, :, 10:13]  # (bsz, num_grippers, 3)
+                gripper_angular_vel = torch.norm(gripper_quat_vel, dim=-1, keepdim=True)  # (bsz, num_grippers, 1)
+                gripper_quat_axis = gripper_quat_vel / (gripper_angular_vel + 1e-10)  # (bsz, num_grippers, 3)
+
+                grid_from_gripper_axis = x_from_gripper - \
+                    (gripper_quat_axis[:, :, None] * x_from_gripper).sum(dim=-1, keepdim=True) * gripper_quat_axis[:, :, None]  # (bsz, num_grippers, num_particles, 3)
+                gripper_v_expand = torch.cross(gripper_quat_vel[:, :, None], grid_from_gripper_axis, dim=-1) + gripper_v_expand
+
+                for i in range(gripper_xyz.shape[1]):
+                    x_gripper_distance_mask_single = x_gripper_distance_mask[:, i]
+                    x[x_gripper_distance_mask_single] = x_in[x_gripper_distance_mask_single] + cfg.sim.dt * gripper_v_expand[:, i][x_gripper_distance_mask_single]
+                    v[x_gripper_distance_mask_single] = gripper_v_expand[:, i][x_gripper_distance_mask_single]
+            
+            if cfg.sim.n_history > 0:
+                assert x_his is not None and v_his is not None
+                if cfg.sim.gripper_points:
+                    x_his_particles = torch.cat([x_his[:, :num_particles_orig].reshape(batch_size, num_particles_orig, -1, 3)[:, :, 1:], x[:, :num_particles_orig, None].detach()], dim=2)
+                    v_his_particles = torch.cat([v_his[:, :num_particles_orig].reshape(batch_size, num_particles_orig, -1, 3)[:, :, 1:], v[:, :num_particles_orig, None].detach()], dim=2)
+                    x_his = x_his_particles.reshape(batch_size, num_particles_orig, -1)
+                    v_his = v_his_particles.reshape(batch_size, num_particles_orig, -1)
+                else:
+                    x_his = torch.cat([x_his.reshape(batch_size, num_particles, -1, 3)[:, :, 1:], x[:, :, None].detach()], dim=2)
+                    v_his = torch.cat([v_his.reshape(batch_size, num_particles, -1, 3)[:, :, 1:], v[:, :, None].detach()], dim=2)
+                    x_his = x_his.reshape(batch_size, num_particles, -1)
+                    v_his = v_his.reshape(batch_size, num_particles, -1)
+            
+            if cfg.sim.gripper_points:
+                x = x[:, :num_particles_orig]
+                v = v[:, :num_particles_orig]
+                enabled = enabled[:, :num_particles_orig]
+
+            colliders_save = colliders.export()
+            colliders_save = {key: torch.from_numpy(colliders_save[key])[0].to(x.device).to(x.dtype) for key in colliders_save}
+
+            xs.append(x.detach().clone())
+            vs.append(v.detach().clone())
+
+        xs = torch.stack(xs, dim=1)  # (batch_size, num_steps_total, num_particles, 3)
+        vs = torch.stack(vs, dim=1)  # (batch_size, num_steps_total, num_particles, 3)
+
+        return xs, vs
diff --git a/src/experiments/real_world/modules_planning/kinematics_utils.py b/src/experiments/real_world/modules_planning/kinematics_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..a23e03865ccfb70af3892a80551b47c1341677e0
--- /dev/null
+++ b/src/experiments/real_world/modules_planning/kinematics_utils.py
@@ -0,0 +1,137 @@
+import os
+import copy
+import time
+import numpy as np
+import transforms3d
+from pathlib import Path
+import open3d as o3d
+
+import warnings
+warnings.filterwarnings("always", category=RuntimeWarning)
+
+import sapien.core as sapien
+
+from nclaw.utils import get_root
+root: Path = get_root(__file__)
+
+def np2o3d(pcd, color=None):
+    # pcd: (n, 3)
+    # color: (n, 3)
+    pcd_o3d = o3d.geometry.PointCloud()
+    pcd_o3d.points = o3d.utility.Vector3dVector(pcd)
+    if color is not None:
+        assert pcd.shape[0] == color.shape[0]
+        assert color.max() <= 1
+        assert color.min() >= 0
+        pcd_o3d.colors = o3d.utility.Vector3dVector(color)
+    return pcd_o3d
+
+
+class KinHelper():
+    def __init__(self, robot_name, headless=True):
+        # load robot
+        if "xarm7" in robot_name:
+            urdf_path = str(root / "assets/robots/xarm7/xarm7.urdf")
+            self.eef_name = 'link7'
+        else:
+            raise RuntimeError('robot name not supported')
+        self.robot_name = robot_name
+
+        # load sapien robot
+        self.engine = sapien.Engine()
+        self.scene = self.engine.create_scene()
+        loader = self.scene.create_urdf_loader()
+        self.sapien_robot = loader.load(urdf_path)
+        self.robot_model = self.sapien_robot.create_pinocchio_model()
+        self.sapien_eef_idx = -1
+        for link_idx, link in enumerate(self.sapien_robot.get_links()):
+            if link.name == self.eef_name:
+                self.sapien_eef_idx = link_idx
+                break
+
+        # load meshes and offsets from urdf_robot
+        self.meshes = {}
+        self.scales = {}
+        self.offsets = {}
+        self.pcd_dict = {}
+        self.tool_meshes = {}
+
+    def compute_fk_sapien_links(self, qpos, link_idx):
+        fk = self.robot_model.compute_forward_kinematics(qpos)
+        link_pose_ls = []
+        for i in link_idx:
+            link_pose_ls.append(self.robot_model.get_link_pose(i).to_transformation_matrix())
+        return link_pose_ls
+
+    def compute_ik_sapien(self, initial_qpos, cartesian, verbose=False):
+        """
+        Compute IK using sapien
+        initial_qpos: (N, ) numpy array
+        cartesian: (6, ) numpy array, x,y,z in meters, r,p,y in radians
+        """
+        tf_mat = np.eye(4)
+        tf_mat[:3, :3] = transforms3d.euler.euler2mat(ai=cartesian[3], aj=cartesian[4], ak=cartesian[5], axes='sxyz')
+        tf_mat[:3, 3] = cartesian[0:3]
+        pose = sapien.Pose.from_transformation_matrix(tf_mat)
+
+        if 'xarm7' in self.robot_name:
+            active_qmask = np.array([True, True, True, True, True, True, True])
+        qpos = self.robot_model.compute_inverse_kinematics(
+            link_index=self.sapien_eef_idx, 
+            pose=pose,
+            initial_qpos=initial_qpos, 
+            active_qmask=active_qmask, 
+            )
+        if verbose:
+            print('ik qpos:', qpos)
+
+        # verify ik
+        fk_pose = self.compute_fk_sapien_links(qpos[0], [self.sapien_eef_idx])[0]
+        
+        if verbose:
+            print('target pose for IK:', tf_mat)
+            print('fk pose for IK:', fk_pose)
+        
+        pose_diff = np.linalg.norm(fk_pose[:3, 3] - tf_mat[:3, 3])
+        rot_diff = np.linalg.norm(fk_pose[:3, :3] - tf_mat[:3, :3])
+        
+        if pose_diff > 0.01 or rot_diff > 0.01:
+            print('ik pose diff:', pose_diff)
+            print('ik rot diff:', rot_diff)
+            warnings.warn('ik pose diff or rot diff too large. Return initial qpos.', RuntimeWarning, stacklevel=2, )
+            return initial_qpos
+        return qpos[0]
+
+
+def test_fk():
+    robot_name = 'xarm7'
+    init_qpos = np.array([0, 0, 0, 0, 0, 0, 0])
+    end_qpos = np.array([0, 0, 0, 0, 0, 0, 0])
+    
+    kin_helper = KinHelper(robot_name=robot_name, headless=False)
+    START_ARM_POSE = [0, 0, 0, 0, 0, 0, 0]
+
+    for i in range(100):
+        curr_qpos = init_qpos + (end_qpos - init_qpos) * i / 100
+        fk = kin_helper.compute_fk_sapien_links(curr_qpos, [kin_helper.sapien_eef_idx])[0]
+        fk_euler = transforms3d.euler.mat2euler(fk[:3, :3], axes='sxyz')
+
+        if i == 0:
+            init_ik_qpos = np.array(START_ARM_POSE)
+        ik_qpos = kin_helper.compute_ik_sapien(init_ik_qpos, np.array(list(fk[:3, 3]) + list(fk_euler)).astype(np.float32))
+        re_fk_pos_mat = kin_helper.compute_fk_sapien_links(ik_qpos, [kin_helper.sapien_eef_idx])[0]
+        re_fk_euler = transforms3d.euler.mat2euler(re_fk_pos_mat[:3, :3], axes='sxyz')
+        re_fk_pos = re_fk_pos_mat[:3, 3]
+        print('re_fk_pos diff:', np.linalg.norm(re_fk_pos - fk[:3, 3]))
+        print('re_fk_euler diff:', np.linalg.norm(np.array(re_fk_euler) - np.array(fk_euler)))
+        
+
+        init_ik_qpos = ik_qpos.copy()
+        qpos_diff = np.linalg.norm(ik_qpos[:6] - curr_qpos[:6])
+        if qpos_diff > 0.01:
+            warnings.warn('qpos diff too large', RuntimeWarning, stacklevel=2, )
+
+        time.sleep(0.1)
+
+if __name__ == "__main__":
+    test_fk()
diff --git a/src/experiments/real_world/modules_planning/planning_env.py b/src/experiments/real_world/modules_planning/planning_env.py
new file mode 100644
index 0000000000000000000000000000000000000000..0eace49a85e40f94410e61bee1847150b7afe4ef
--- /dev/null
+++ b/src/experiments/real_world/modules_planning/planning_env.py
@@ -0,0 +1,709 @@
+from typing import Callable, List
+from omegaconf import DictConfig, OmegaConf
+from enum import Enum
+import torch
+import numpy as np
+import multiprocess as mp
+import time
+import threading
+import cv2
+import pygame
+import os
+import pickle
+import subprocess
+from pathlib import Path
+from copy import deepcopy
+import open3d as o3d
+
+from pgnd.utils import get_root, mkdir
+root: Path = get_root(__file__)
+
+from camera.multi_realsense import MultiRealsense
+from camera.single_realsense import SingleRealsense
+
+from modules_planning.segment_perception import SegmentPerception
+from modules_planning.planning_module import PlanningModule
+from modules_planning.xarm_controller import XarmController
+from modules_planning.udp_util import udpReceiver, udpSender
+from modules_planning.common.communication import XARM_STATE_PORT, XARM_CONTROL_PORT, XARM_CONTROL_PORT_L, XARM_CONTROL_PORT_R
+from modules_planning.common.xarm import GRIPPER_OPEN_MIN, GRIPPER_OPEN_MAX, POSITION_UPDATE_INTERVAL, COMMAND_CHECK_INTERVAL
+
+
+class EnvEnum(Enum):
+    NONE = 0
+    INFO = 1
+    DEBUG = 2
+    VERBOSE = 3
+
+
+class RobotPlanningEnv(mp.Process):
+
+    def __init__(
+        self, 
+        cfg: DictConfig,
+        exp_root: str,
+        ckpt_path: Path | str,
+
+        realsense: MultiRealsense | SingleRealsense | None = None,
+        shm_manager: mp.managers.SharedMemoryManager | None = None,
+        serial_numbers: list[str] | None= None,
+        resolution: tuple[int, int] = (1280, 720),
+        capture_fps: int = 30,
+        enable_depth: bool = True,
+        enable_color: bool = True,
+
+        record_fps: int | None = 0,
+        record_time: float | None = 60 * 10,  # 10 minutes
+        text_prompts: str = "white cotton rope.",
+        show_annotation: bool = True,
+
+        use_robot: bool = False,
+        robot_ip: str = '192.168.1.196',
+        gripper_enable: bool = False,
+        calibrate_result_dir: Path = root / "log" / "latest_calibration",
+        debug: bool | int | None = False,
+        
+        bimanual: bool = False,
+        bimanual_robot_ip: List[str] = ['192.168.1.196', '192.168.1.224'],
+
+        construct_target: bool = False,
+
+    ) -> None:
+
+        # Debug level
+        self.debug = 0 if debug is None else (2 if debug is True else debug)
+
+        self.cfg = cfg
+        self.exp_root = Path(exp_root)
+
+        if os.path.exists(self.exp_root.parent / "target" / "target.pcd"):
+            mkdir(self.exp_root / "target", overwrite=False, resume=True)
+            os.system(f"cp {self.exp_root.parent / 'target' / 'target.pcd'} {self.exp_root / 'target'}")
+        else:
+            assert construct_target, "target.pcd not found"
+        self.construct_target = construct_target
+        if construct_target:
+            mkdir(self.exp_root.parent / "target", overwrite=False, resume=False)
+
+        self.torch_device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+        self.bimanual = bimanual
+        if self.bimanual:
+            self.bbox = np.array([[0.0, 0.7], [-0.35, 0.45 + 0.75], [-0.8, 0.0]])
+        else:
+            self.bbox = np.array([[0.0, 0.7], [-0.35, 0.45], [-0.8, 0.0]])
+        
+        self.text_prompts = text_prompts
+        self.show_annotation = show_annotation
+
+        self.capture_fps = capture_fps
+        self.record_fps = record_fps
+
+        self.state = mp.Manager().dict() # should be main explict exposed variable to the child class / process
+
+        # Realsense camera setup
+        # camera is always required for real env
+        if realsense is not None:
+            assert isinstance(realsense, MultiRealsense) or isinstance(realsense, SingleRealsense)
+            self.realsense = realsense
+            self.serial_numbers = list(self.realsense.cameras.keys())
+        else:
+            self.realsense = MultiRealsense(
+                shm_manager=shm_manager,
+                serial_numbers=serial_numbers,
+                resolution=resolution,
+                capture_fps=capture_fps,
+                enable_depth=enable_depth,
+                enable_color=enable_color,
+                verbose=self.debug > EnvEnum.VERBOSE.value
+            )
+            self.serial_numbers = list(self.realsense.cameras.keys())
+    
+        # manual
+        self.realsense.set_exposure(exposure=60, gain=60)  # 100: bright, 60: dark
+        self.realsense.set_white_balance(3800)
+
+        # base calibration
+        self.calibrate_result_dir = calibrate_result_dir
+        with open(f'{self.calibrate_result_dir}/base.pkl', 'rb') as f:
+            base = pickle.load(f)
+        if self.bimanual:
+            R_leftbase2board = base['R_leftbase2world']
+            t_leftbase2board = base['t_leftbase2world']
+            R_rightbase2board = base['R_rightbase2world']
+            t_rightbase2board = base['t_rightbase2world']
+            leftbase2world_mat = np.eye(4)
+            leftbase2world_mat[:3, :3] = R_leftbase2board
+            leftbase2world_mat[:3, 3] = t_leftbase2board
+            self.state["b2w_l"] = leftbase2world_mat
+            rightbase2world_mat = np.eye(4)
+            rightbase2world_mat[:3, :3] = R_rightbase2board
+            rightbase2world_mat[:3, 3] = t_rightbase2board
+            self.state["b2w_r"] = rightbase2world_mat
+        else:
+            R_base2board = base['R_base2world']
+            t_base2board = base['t_base2world']
+            base2world_mat = np.eye(4)
+            base2world_mat[:3, :3] = R_base2board
+            base2world_mat[:3, 3] = t_base2board
+            self.state["b2w"] = base2world_mat
+
+        # camera calibration
+        extr_list = []
+        with open(f'{self.calibrate_result_dir}/rvecs.pkl', 'rb') as f:
+            rvecs = pickle.load(f)
+        with open(f'{self.calibrate_result_dir}/tvecs.pkl', 'rb') as f:
+            tvecs = pickle.load(f)
+        for i in range(len(self.serial_numbers)):
+            device = self.serial_numbers[i]
+            R_world2cam = cv2.Rodrigues(rvecs[device])[0]
+            t_world2cam = tvecs[device][:, 0]
+            extr_mat = np.eye(4)
+            extr_mat[:3, :3] = R_world2cam
+            extr_mat[:3, 3] = t_world2cam
+            extr_list.append(extr_mat)
+        self.state["extr"] = np.stack(extr_list)
+
+        # save calibration
+        mkdir(self.exp_root / "calibration", overwrite=False, resume=False)
+        subprocess.run(f'cp -r {self.calibrate_result_dir}/* {str(self.exp_root)}/calibration', shell=True)
+
+        # Perception setup
+        self.perception = SegmentPerception(
+            realsense=self.realsense,
+            capture_fps=self.realsense.capture_fps,  # mush be the same as realsense capture fps 
+            record_fps=record_fps,
+            record_time=record_time,
+            text_prompts=self.text_prompts,
+            show_annotation=self.show_annotation,
+            bbox=self.bbox,
+            device=self.torch_device,
+            verbose=self.debug > EnvEnum.VERBOSE.value)
+
+        # Robot setup
+        self.use_robot = use_robot
+        if use_robot:
+            if bimanual:
+                self.left_xarm_controller = XarmController(
+                    start_time=time.time(),
+                    ip=bimanual_robot_ip[0],
+                    gripper_enable=gripper_enable,
+                    mode='3D',
+                    command_mode='cartesian',
+                    robot_id=0,
+                    verbose=True,
+                )
+                self.right_xarm_controller = XarmController(
+                    start_time=time.time(),
+                    ip=bimanual_robot_ip[1],
+                    gripper_enable=gripper_enable,
+                    mode='3D',
+                    command_mode='cartesian',
+                    robot_id=1,
+                    verbose=True,
+                )
+                self.xarm_controller = None
+            else:
+                self.xarm_controller = XarmController(
+                    start_time=time.time(),
+                    ip=robot_ip,
+                    gripper_enable=gripper_enable,
+                    mode='3D',
+                    command_mode='cartesian',
+                    verbose=True,
+                )
+                self.left_xarm_controller = None
+                self.right_xarm_controller = None
+        else:
+            self.left_xarm_controller = None
+            self.right_xarm_controller = None
+            self.xarm_controller = None
+
+        # subprocess can only start a process object created by current process
+        self._real_alive = mp.Value('b', False)
+
+        self.start_time = 0
+        mp.Process.__init__(self)
+        self._alive = mp.Value('b', False)
+
+        # pygame
+        # Initialize a separate Pygame window for image display
+        img_w, img_h = 848, 480
+        col_num = 2
+        self.screen_width, self.screen_height = img_w * col_num, img_h * len(self.realsense.serial_numbers)
+        self.image_window = None
+
+        # Shared memory for image data
+        self.image_data = mp.Array('B', np.zeros((self.screen_height, self.screen_width, 3), dtype=np.uint8).flatten())
+
+        # robot eef
+        self.eef_point = np.array([[0.0, 0.0, 0.175]])  # the eef point in the gripper frame
+
+        # planning_module setup
+        self.planning_module = PlanningModule(
+            cfg=cfg,
+            exp_root=self.exp_root,
+            ckpt_path=ckpt_path,
+            device=self.torch_device,
+            bimanual=bimanual,
+            bbox=self.bbox,
+            eef_point=self.eef_point,
+            debug=self.debug
+        )
+        self.planning_module.set_target(str(self.exp_root / "target" / "target.pcd"))
+        self.command = []
+        self.command_sender = None
+        self.command_sender_left = None
+        self.command_sender_right = None
+
+    def real_start(self, start_time) -> None:
+        self._real_alive.value = True
+        print("starting real env")
+        
+        # Realsense camera setup
+        self.realsense.start()
+        self.realsense.restart_put(start_time + 1)
+        time.sleep(2)
+
+        # Perception setup
+        if self.perception is not None:
+            self.perception.start()
+        self.perception.update_extrinsics(self.state["extr"])
+    
+        # Robot setup
+        if self.use_robot:
+            if self.bimanual:
+                assert self.left_xarm_controller is not None
+                assert self.right_xarm_controller is not None
+                self.left_xarm_controller.start()
+                self.right_xarm_controller.start()
+            else:
+                assert self.xarm_controller is not None
+                self.xarm_controller.start()
+        
+        while not self.real_alive:
+            self._real_alive.value = True
+            print(".", end="")
+            time.sleep(0.5)
+        
+        # get intrinsics
+        intrs = self.get_intrinsics()
+        intrs = np.array(intrs)
+        np.save(self.exp_root / "calibration" / "intrinsics.npy", intrs)
+
+        print("real env started")
+
+        self.update_real_state_t = threading.Thread(name="update_real_state", target=self.update_real_state)
+        self.update_real_state_t.start()
+
+    def real_stop(self, wait=False) -> None:
+        self._real_alive.value = False
+        if self.use_robot:
+            if self.bimanual and self.left_xarm_controller and self.left_xarm_controller.is_controller_alive:
+                self.left_xarm_controller.stop()
+            if self.bimanual and self.right_xarm_controller and self.right_xarm_controller.is_controller_alive:
+                self.right_xarm_controller.stop()
+            if not self.bimanual and self.xarm_controller and self.xarm_controller.is_controller_alive:
+                self.xarm_controller.stop()
+        if self.perception is not None and self.perception.alive.value:
+            self.perception.stop()
+        self.realsense.stop(wait=False)
+
+        self.image_display_thread.join()
+        self.update_real_state_t.join()
+        print("real env stopped")
+
+    @property
+    def real_alive(self) -> bool:
+        alive = self._real_alive.value
+        if self.perception is not None:
+            alive = alive and self.perception.alive.value
+        if self.use_robot:
+            controller_alive = \
+                (self.bimanual and self.left_xarm_controller and self.left_xarm_controller.is_controller_alive \
+                    and self.right_xarm_controller and self.right_xarm_controller.is_controller_alive) \
+                or (not self.bimanual and self.xarm_controller and self.xarm_controller.is_controller_alive)
+            alive = alive and controller_alive
+        self._real_alive.value = alive
+        return self._real_alive.value
+
+    def _update_perception(self) -> None:
+        if self.perception.alive.value and self.perception.do_process.value:
+            if not self.perception.perception_q.empty():
+                self.state["perception_out"] = {
+                    "time": time.time(),
+                    "value": self.perception.perception_q.get()
+                }
+        return
+
+    def _update_robot(self) -> None:
+        if self.bimanual:
+            assert self.left_xarm_controller is not None
+            assert self.right_xarm_controller is not None
+            if self.left_xarm_controller.is_controller_alive and self.right_xarm_controller.is_controller_alive:
+                if not self.left_xarm_controller.cur_trans_q.empty() and not self.right_xarm_controller.cur_trans_q.empty():
+                    self.state["robot_out"] = {
+                        "time": time.time(),
+                        "left_value": self.left_xarm_controller.cur_trans_q.get(),
+                        "right_value": self.right_xarm_controller.cur_trans_q.get()
+                    }
+                if not self.left_xarm_controller.cur_gripper_q.empty() and not self.right_xarm_controller.cur_trans_q.empty():
+                    self.state["gripper_out"] = {
+                        "time": time.time(),
+                        "left_value": self.left_xarm_controller.cur_gripper_q.get(),
+                        "right_value": self.right_xarm_controller.cur_gripper_q.get()
+                    }
+        else:
+            assert self.xarm_controller is not None
+            if self.xarm_controller.is_controller_alive:
+                if not self.xarm_controller.cur_trans_q.empty():
+                    self.state["robot_out"] = {
+                        "time": time.time(),
+                        "value": self.xarm_controller.cur_trans_q.get()
+                    }
+                if not self.xarm_controller.cur_gripper_q.empty():
+                    self.state["gripper_out"] = {
+                        "time": time.time(),
+                        "value": self.xarm_controller.cur_gripper_q.get()
+                    }
+        return
+
+    def update_real_state(self) -> None:
+        while self.real_alive:
+            try:
+                if self.use_robot:
+                    self._update_robot()
+                if self.perception is not None:
+                    self._update_perception()
+            except BaseException as e:
+                print(f"Error in update_real_state: {e.with_traceback()}")
+                break
+        print("update_real_state stopped")
+
+    def display_image(self):
+        pygame.init()
+        self.image_window = pygame.display.set_mode((self.screen_width, self.screen_height))
+        pygame.display.set_caption('Image Display Window')
+        while self._alive.value:
+            # Extract image data from the shared array
+            image = np.frombuffer(self.image_data.get_obj(), dtype=np.uint8).reshape((self.screen_height, self.screen_width, 3))
+            pygame_image = pygame.surfarray.make_surface(image.swapaxes(0, 1))
+
+            # Blit the image to the window
+            self.image_window.blit(pygame_image, (0, 0))
+            pygame.display.update()
+
+            # Handle events (e.g., close window)
+            for event in pygame.event.get():
+                if event.type == pygame.QUIT:
+                    self.stop()
+                    pygame.quit()
+                    return
+
+            time.sleep(1 / self.realsense.capture_fps)  # 30 FPS
+        print("Image display stopped")
+
+    def start_image_display(self):
+        # Start a thread for the image display loop
+        self.image_display_thread = threading.Thread(name="display_image", target=self.display_image)
+        self.image_display_thread.start()
+
+    def run(self) -> None:
+
+        robot_record_dir = None
+        if self.bimanual:
+            self.command_sender_left = udpSender(port=XARM_CONTROL_PORT_L)
+            self.command_sender_right = udpSender(port=XARM_CONTROL_PORT_R)
+        else:
+            self.command_sender = udpSender(port=XARM_CONTROL_PORT)
+
+        # initialize images
+        rgbs = []  # bgr
+        depths = []
+        resolution = self.realsense.resolution
+        for i in range(len(self.realsense.serial_numbers)):
+            rgbs.append(np.zeros((resolution[1], resolution[0], 3), np.uint8))
+            depths.append(np.zeros((resolution[1], resolution[0]), np.uint16))
+
+        fps = self.record_fps if self.record_fps and self.record_fps > 0 else self.realsense.capture_fps  # visualization fps
+        idx = 0
+
+        while self.alive:
+            try:
+                tic = time.time()
+                state = deepcopy(self.state)
+                if self.bimanual:
+                    b2w_l = state["b2w_l"]
+                    b2w_r = state["b2w_r"]
+                    b2w = None
+                else:
+                    b2w = state["b2w"]
+                    b2w_l = None
+                    b2w_r = None
+
+                # update images from realsense to shared memory
+                perception_out = state.get("perception_out", None)
+                robot_out = state.get("robot_out", None)
+                gripper_out = state.get("gripper_out", None)
+
+                # clear previous images
+                if perception_out is not None:
+                    self.state["perception_out"] = None
+
+                intrinsics = self.get_intrinsics()
+                state["intr"] = intrinsics
+                self.state["intr"] = intrinsics
+                self.perception.update_intrinsics(intrinsics)
+
+                update_perception = True
+                if update_perception and perception_out is not None:
+                    print("update image display")
+                    for k in range(len(perception_out['value']['color'])):
+                        rgbs[k] = perception_out['value']['color'][k].copy()
+                        depths[k] = perception_out['value']['depth'][k].copy()
+                        intr = intrinsics[k]
+
+                        l = 0.1
+                        origin = state["extr"][k] @ np.array([0, 0, 0, 1])
+                        x_axis = state["extr"][k] @ np.array([l, 0, 0, 1])
+                        y_axis = state["extr"][k] @ np.array([0, l, 0, 1])
+                        z_axis = state["extr"][k] @ np.array([0, 0, l, 1])
+                        origin = origin[:3] / origin[2]
+                        x_axis = x_axis[:3] / x_axis[2]
+                        y_axis = y_axis[:3] / y_axis[2]
+                        z_axis = z_axis[:3] / z_axis[2]
+                        origin = intr @ origin
+                        x_axis = intr @ x_axis
+                        y_axis = intr @ y_axis
+                        z_axis = intr @ z_axis
+                        cv2.line(rgbs[k], (int(origin[0]), int(origin[1])), (int(x_axis[0]), int(x_axis[1])), (255, 0, 0), 2)
+                        cv2.line(rgbs[k], (int(origin[0]), int(origin[1])), (int(y_axis[0]), int(y_axis[1])), (0, 255, 0), 2)
+                        cv2.line(rgbs[k], (int(origin[0]), int(origin[1])), (int(z_axis[0]), int(z_axis[1])), (0, 0, 255), 2)
+                        if self.use_robot:
+                            eef_points = np.concatenate([self.eef_point, np.ones((self.eef_point.shape[0], 1))], axis=1)  # (n, 4)
+                            eef_colors = [(0, 255, 255)]
+
+                            eef_axis = np.array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0]])  # (3, 4)
+                            eef_axis_colors = [(0, 0, 255), (0, 255, 0), (255, 0, 0)]
+
+                            if robot_out is not None:
+                                assert gripper_out is not None
+                                eef_points_world_vis = []
+                                eef_points_vis = []
+                                if self.bimanual:
+                                    left_eef_world_list = []
+                                    right_eef_world_list = []
+                                    assert b2w_l is not None
+                                    assert b2w_r is not None
+                                    for val, b2w, eef_world_list in zip(["left_value", "right_value"], [b2w_l, b2w_r], [left_eef_world_list, right_eef_world_list]):
+                                        e2b = robot_out[val]  # (4, 4)
+                                        eef_points_world = (b2w @ e2b @ eef_points.T).T[:, :3]  # (n, 3)
+                                        eef_points_vis.append(eef_points)
+                                        eef_points_world_vis.append(eef_points_world)
+                                        eef_orientation_world = (b2w[:3, :3] @ e2b[:3, :3] @ eef_axis[:, :3].T).T  # (3, 3)
+                                        eef_world = np.concatenate([eef_points_world, eef_orientation_world], axis=0)  # (n+3, 3)
+                                        eef_world_list.append(eef_world)
+                                    left_eef_world = np.concatenate(left_eef_world_list, axis=0)  # (n+3, 3)
+                                    right_eef_world = np.concatenate(right_eef_world_list, axis=0)  # (n+3, 3)
+                                    eef_world = np.concatenate([left_eef_world, right_eef_world], axis=0)  # (2n+6, 3)
+                                else:
+                                    assert b2w is not None
+                                    e2b = robot_out["value"]  # (4, 4)
+                                    eef_points_world = (b2w @ e2b @ eef_points.T).T[:, :3]  # (n, 3)
+                                    eef_points_vis.append(eef_points)
+                                    eef_points_world_vis.append(eef_points_world)
+                                    eef_orientation_world = (b2w[:3, :3] @ e2b[:3, :3] @ eef_axis[:, :3].T).T  # (3, 3)
+                                    eef_world = np.concatenate([eef_points_world, eef_orientation_world], axis=0)  # (n+3, 3)
+                                
+                                # add gripper
+                                if self.bimanual:
+                                    left_gripper = gripper_out["left_value"]
+                                    right_gripper = gripper_out["right_value"]
+                                    gripper_world = np.array([left_gripper, right_gripper, 0.0])[None, :]  # (1, 3)
+                                else:
+                                    gripper = gripper_out["value"]
+                                    gripper_world = np.array([gripper, 0.0, 0.0])[None, :]  # (1, 3)
+
+                                eef_world = np.concatenate([eef_world, gripper_world], axis=0)  # (n+4, 3) or (2n+7, 3)
+                                if robot_record_dir is not None:
+                                    np.savetxt(robot_record_dir / f"{robot_out['time']:.3f}.txt", eef_world, fmt="%.6f")
+                                
+                                eef_points_vis = np.concatenate(eef_points_vis, axis=0)
+                                eef_points_world_vis = np.concatenate(eef_points_world_vis, axis=0)
+                                eef_points_world_vis = np.concatenate([eef_points_world_vis, np.ones((eef_points_world_vis.shape[0], 1))], axis=1)  # (n, 4)
+                                eef_colors = eef_colors * eef_points_world_vis.shape[0]
+                                
+                                if self.bimanual:
+                                    for point_orig, point, color, val, b2w in zip(eef_points_vis, eef_points_world_vis, eef_colors, ["left_value", "right_value"], [b2w_l, b2w_r]):
+                                        e2b = robot_out[val]  # (4, 4)
+                                        point = state["extr"][k] @ point
+                                        point = point[:3] / point[2]
+                                        point = intr @ point
+                                        cv2.circle(rgbs[k], (int(point[0]), int(point[1])), 2, color, -1)
+                                    
+                                        # draw eef axis
+                                        for axis, color in zip(eef_axis, eef_axis_colors):
+                                            eef_point_axis = point_orig + 0.1 * axis
+                                            eef_point_axis_world = (b2w @ e2b @ eef_point_axis).T
+                                            eef_point_axis_world = state["extr"][k] @ eef_point_axis_world
+                                            eef_point_axis_world = eef_point_axis_world[:3] / eef_point_axis_world[2]
+                                            eef_point_axis_world = intr @ eef_point_axis_world
+                                            cv2.line(rgbs[k], 
+                                                (int(point[0]), int(point[1])), 
+                                                (int(eef_point_axis_world[0]), int(eef_point_axis_world[1])), 
+                                                color, 2)
+                                else:
+                                    point_orig, point, color, val, b2w = eef_points_vis[0], eef_points_world_vis[0], eef_colors[0], "value", b2w
+                                    e2b = robot_out[val]  # (4, 4)
+                                    point = state["extr"][k] @ point
+                                    point = point[:3] / point[2]
+                                    point = intr @ point
+                                    cv2.circle(rgbs[k], (int(point[0]), int(point[1])), 2, color, -1)
+                                
+                                    # draw eef axis
+                                    for axis, color in zip(eef_axis, eef_axis_colors):
+                                        eef_point_axis = point_orig + 0.1 * axis
+                                        eef_point_axis_world = (b2w @ e2b @ eef_point_axis).T
+                                        eef_point_axis_world = state["extr"][k] @ eef_point_axis_world
+                                        eef_point_axis_world = eef_point_axis_world[:3] / eef_point_axis_world[2]
+                                        eef_point_axis_world = intr @ eef_point_axis_world
+                                        cv2.line(rgbs[k], 
+                                            (int(point[0]), int(point[1])), 
+                                            (int(eef_point_axis_world[0]), int(eef_point_axis_world[1])), 
+                                            color, 2)
+
+                    row_imgs = []
+                    for row in range(len(self.realsense.serial_numbers)):
+                        row_imgs.append(
+                            np.hstack(
+                                (cv2.cvtColor(rgbs[row], cv2.COLOR_BGR2RGB), 
+                                cv2.applyColorMap(cv2.convertScaleAbs(depths[row], alpha=0.03), cv2.COLORMAP_JET))
+                            )
+                        )
+                    combined_img = np.vstack(row_imgs)
+                    combined_img = cv2.resize(combined_img, (self.screen_width,self.screen_height))
+                    np.copyto(
+                        np.frombuffer(self.image_data.get_obj(), dtype=np.uint8).reshape((self.screen_height, self.screen_width, 3)), 
+                        combined_img
+                    )
+                
+                # save target
+                if self.construct_target and perception_out is not None and robot_out is not None:
+                    assert os.path.exists(self.exp_root.parent / "target")
+                    save_dir = str(self.exp_root.parent / "target" / "target.pcd")
+                    pts = perception_out['value']['pts']
+                    print(f"target pts: {pts}")
+                    pts = np.concatenate(pts, axis=0)
+                    pcd = o3d.geometry.PointCloud()
+                    pcd.points = o3d.utility.Vector3dVector(pts)
+                    o3d.io.write_point_cloud(save_dir, pcd)
+                    
+                    mkdir(self.exp_root.parent / "target" / "vis", overwrite=False, resume=True)
+                    eef_points_world_vis = []
+                    eef_points = np.concatenate([self.eef_point, np.ones((self.eef_point.shape[0], 1))], axis=1)  # (n, 4)
+                    eef_axis = np.array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0]])  # (3, 4)
+                    if self.bimanual:
+                        left_eef_world_list = []
+                        right_eef_world_list = []
+                        assert b2w_l is not None
+                        assert b2w_r is not None
+                        for val, b2w, eef_world_list in zip(["left_value", "right_value"], [b2w_l, b2w_r], [left_eef_world_list, right_eef_world_list]):
+                            e2b = robot_out[val]  # (4, 4)
+                            eef_points_world = (b2w @ e2b @ eef_points.T).T[:, :3]  # (n, 3)
+                            eef_points_world_vis.append(eef_points_world)
+                            eef_orientation_world = (b2w[:3, :3] @ e2b[:3, :3] @ eef_axis[:, :3].T).T  # (3, 3)
+                            eef_world = np.concatenate([eef_points_world, eef_orientation_world], axis=0)  # (n+3, 3)
+                            eef_world_list.append(eef_world)
+                        left_eef_world = np.concatenate(left_eef_world_list, axis=0)  # (n+3, 3)
+                        right_eef_world = np.concatenate(right_eef_world_list, axis=0)  # (n+3, 3)
+                        eef_world = np.concatenate([left_eef_world, right_eef_world], axis=0)  # (2n+6, 3)
+                    else:
+                        assert b2w is not None
+                        e2b = robot_out["value"]  # (4, 4)
+                        eef_points_world = (b2w @ e2b @ eef_points.T).T[:, :3]  # (n, 3)
+                        eef_points_world_vis.append(eef_points_world)
+                        eef_orientation_world = (b2w[:3, :3] @ e2b[:3, :3] @ eef_axis[:, :3].T).T  # (3, 3)
+                        eef_world = np.concatenate([eef_points_world, eef_orientation_world], axis=0)  # (n+3, 3)
+                    np.savetxt(str(self.exp_root.parent / "target" / "vis" / f"robot.txt"), eef_world, fmt="%.6f")
+
+                    for k in range(len(perception_out['value']['color'])):
+                        rgbs[k] = perception_out['value']['color'][k]
+                        depths[k] = perception_out['value']['depth'][k]
+                        rgb_save_dir = str(self.exp_root.parent / "target" / "vis" / f"rgb_{k}.png")
+                        depth_save_dir = str(self.exp_root.parent / "target" / "vis" / f"depth_{k}.png")
+                        cv2.imwrite(rgb_save_dir, rgbs[k])
+                        cv2.imwrite(depth_save_dir, depths[k])
+                    
+                    intr_list = []
+                    extr_list = []
+                    for k in range(len(perception_out['value']['color'])):
+                        intr = intrinsics[k]
+                        extr = state["extr"][k]
+                        intr_list.append(intr)
+                        extr_list.append(extr)
+                    np.save(str(self.exp_root.parent / "target" / "vis" / "intrinsics.npy"), np.stack(intr_list))
+                    np.save(str(self.exp_root.parent / "target" / "vis" / "extrinsics.npy"), np.stack(extr_list))
+
+                    print(f"target saved to {save_dir}")
+                    time.sleep(5)
+                    continue
+                
+                # do planning
+                if perception_out is not None and robot_out is not None and gripper_out is not None:
+                    if len(perception_out['value']['pts']) > 0:
+                        self.perception.do_process.value = False  # pause perception
+                        command = self.planning_module.get_command(state, save_dir=f'{self.exp_root}/interaction_{idx:02d}', is_first_iter=(idx == 0))
+                        if self.bimanual:
+                            self.command_sender_left.send(command[0])
+                            self.command_sender_right.send(command[1])
+                        else:
+                            self.command_sender.send(command[0])
+                        idx += 1
+                        time.sleep(10)  # TODO execution time
+                        self.perception.do_process.value = True  # resume perception
+                    else:
+                        print(f'no points detected in perception_out: {perception_out["value"]["pts"]}')
+                else:
+                    print(f'perception_out is None: {perception_out is None}', end=' ')
+                    print(f'robot_out is None: {robot_out is None}', end=' ')
+                    print(f'gripper_out is None: {gripper_out is None}')
+
+                time.sleep(max(0, 1 / fps - (time.time() - tic)))
+
+            except BaseException as e:
+                print(f"Error in robot planning env: {e.with_traceback()}")
+                break
+
+        if self.bimanual:
+            assert self.command_sender_left is not None
+            assert self.command_sender_right is not None
+            self.command_sender_left.close()
+            self.command_sender_right.close()
+        else:
+            assert self.command_sender is not None
+            self.command_sender.close()
+        self.stop()
+        print("RealEnv process stopped")
+
+    def get_intrinsics(self):
+        return self.realsense.get_intrinsics()
+
+    def get_extrinsics(self):
+        return self.state["extr"]
+
+    @property
+    def alive(self) -> bool:
+        alive = self._alive.value and self.real_alive
+        self._alive.value = alive
+        return alive
+
+    def start(self) -> None:
+        self.start_time = time.time()
+        self._alive.value = True
+        self.real_start(time.time())
+        self.start_image_display()
+        super().start()
+
+    def stop(self) -> None:
+        self._alive.value = False
+        self.real_stop()
diff --git a/src/experiments/real_world/modules_planning/planning_module.py b/src/experiments/real_world/modules_planning/planning_module.py
new file mode 100644
index 0000000000000000000000000000000000000000..5871955ffe830d4cb62cb4fe876f5bb5162a0535
--- /dev/null
+++ b/src/experiments/real_world/modules_planning/planning_module.py
@@ -0,0 +1,570 @@
+from pathlib import Path
+import open3d as o3d
+from threadpoolctl import threadpool_limits
+import multiprocess as mp
+import threading
+from threading import Lock
+
+import os
+import numpy as np
+from copy import deepcopy
+from functools import partial
+import torch
+from torch import Tensor
+import torch.nn as nn
+import torch.nn.functional as F
+import transforms3d
+import kornia
+
+from pgnd.utils import get_root
+root: Path = get_root(__file__)
+
+from modules_planning.dynamics_module import DynamicsModule, fps
+from utils.planning_utils import batch_chamfer_dist
+
+
+class PlanningModule:
+
+    def __init__(self, 
+            cfg, 
+            device,
+            exp_root,
+            ckpt_path,
+            use_robot=False,
+            bimanual=True,
+            bbox=None,
+            eef_point=None,
+            debug=False,
+        ):
+        super().__init__()
+        self.cfg = cfg
+        self.exp_root = exp_root
+        self.ckpt_path = ckpt_path
+        self.bimanual = bimanual
+        self.use_robot = use_robot
+        self.debug = debug
+        self.eef_point = eef_point
+        self.torch_device = device
+
+        assert bbox is not None
+        self.bbox = torch.tensor(bbox, dtype=torch.float32, device=self.torch_device)
+        
+        self.repeated_action = False  # allow non-repetitive action
+
+        self.n_look_ahead = 10  # num_steps_total
+        self.n_sample = 32
+        self.n_sample_chunk = 32
+        self.n_chunk = np.ceil(self.n_sample / self.n_sample_chunk).astype(int)
+        self.n_update_iter = 20
+
+        self.reward_weight = 10.0
+
+        self.target_state = torch.empty(0)
+
+        self.state = None
+        self.pts = torch.empty(0)
+        self.eef_xyz = torch.empty(0)  # (n_grippers, 3)
+        self.eef_rot = torch.empty(0)  # (n_grippers, 3, 3)
+        self.eef_gripper = torch.empty(0)  # (n_grippers,)
+
+        self.dynamics_module = DynamicsModule(cfg, exp_root=exp_root, ckpt_path=ckpt_path, batch_size=self.n_sample_chunk, num_steps_total=self.n_look_ahead)
+        self.dynamics_module.reset_model(self.n_look_ahead)
+        self.xyz_noise_level = 0.002
+        self.quat_noise_level = 0.001
+        self.gripper_noise_level = 0.0
+
+    def set_target(self, pcd_path):
+        pcd = o3d.io.read_point_cloud(pcd_path)
+        target_state = np.array(pcd.points)
+        if len(target_state) == 0:
+            print('target state is empty')
+            return
+        target_state = torch.tensor(target_state, dtype=torch.float32, device=self.torch_device)
+        fps_idx = fps(target_state, 1000, device=self.torch_device, random_start=False)
+        target_state = target_state[fps_idx]
+        self.target_state = target_state.clone()
+        self.dynamics_module.set_target_state(target_state)
+
+    def model_rollout(self, act_seqs, visualize_pv=False):
+        
+        pts = self.pts.clone()
+        n_grippers = self.cfg.sim.num_grippers
+
+        n_sample = act_seqs.shape[0]
+        eef_xyz = act_seqs[:, :, :n_grippers * 3].reshape(n_sample, self.n_look_ahead, n_grippers, 3)
+        eef_rot = act_seqs[:, :, n_grippers * 3:n_grippers * (3 + 3 * 3)].reshape(n_sample, self.n_look_ahead, n_grippers, 3, 3)
+        eef_gripper = act_seqs[:, :, n_grippers * (3 + 3 * 3):].reshape(n_sample, self.n_look_ahead, n_grippers, 1)
+
+        eef_xyz_now = self.eef_xyz.clone()
+        eef_rot_now = self.eef_rot.clone()
+        eef_gripper_now = self.eef_gripper.clone()
+        eef_xyz = torch.cat([eef_xyz_now.repeat(n_sample, 1, 1, 1), eef_xyz], dim=1)  # (n_sample, n_look_forward + 1, n_grippers, 3)
+        eef_rot = torch.cat([eef_rot_now.repeat(n_sample, 1, 1, 1, 1), eef_rot], dim=1)  # (n_sample, n_look_forward + 1, n_grippers, 3, 3)
+        eef_gripper = torch.cat([eef_gripper_now[:, None].repeat(n_sample, 1, 1, 1), eef_gripper], dim=1)  # (n_sample, n_look_forward + 1, n_grippers, 1)
+        assert eef_xyz.shape[1] == eef_rot.shape[1] == eef_gripper.shape[1] == self.n_look_ahead + 1
+
+        x, v = self.dynamics_module.rollout(pts, eef_xyz, eef_rot, eef_gripper, pts_his=None, visualize_pv=visualize_pv)  # (n_sample, n_look_forward, n_pts, 3)
+        model_out = {
+            'x': x,
+        }
+        return model_out
+
+
+    def sample_action_seq(self, act_seq, iter_index=0):
+        # get action
+        n_grippers = self.cfg.sim.num_grippers
+        eef_xyz = act_seq[:, :n_grippers * 3].reshape(self.n_look_ahead, n_grippers, 3)
+        eef_rot = act_seq[:, n_grippers * 3:n_grippers * (3 + 3 * 3)].reshape(self.n_look_ahead, n_grippers, 3, 3)
+        eef_quat = kornia.geometry.conversions.rotation_matrix_to_quaternion(eef_rot)
+        eef_gripper = act_seq[:, n_grippers * (3 + 3 * 3):].reshape(self.n_look_ahead, n_grippers, 1)
+
+        # add noise
+        n_sample = self.n_sample_chunk
+        
+        if self.repeated_action:  # default: linear repeating action
+            eef_xyz_delta = torch.randn((n_sample, eef_xyz.shape[1], eef_xyz.shape[2]), device=self.torch_device, dtype=torch.float32) * self.xyz_noise_level
+            eef_quat_delta = torch.randn((n_sample, eef_quat.shape[1], eef_quat.shape[2]), device=self.torch_device, dtype=torch.float32) * self.quat_noise_level
+            eef_gripper_delta = torch.randn((n_sample, eef_gripper.shape[1], eef_gripper.shape[2]), device=self.torch_device, dtype=torch.float32) * self.gripper_noise_level
+            eef_xyz_delta = eef_xyz_delta[:, None].repeat(1, self.n_look_ahead, 1, 1)
+            eef_quat_delta = eef_quat_delta[:, None].repeat(1, self.n_look_ahead, 1, 1)
+            eef_gripper_delta = eef_gripper_delta[:, None].repeat(1, self.n_look_ahead, 1, 1)
+
+        else:  # segmented repeated action
+            n_parts = 4
+            eef_xyz_delta_list = []
+            eef_quat_delta_list = []
+            eef_gripper_delta_list = []
+            for p in range(n_parts):
+                p_len = self.n_look_ahead // n_parts if p < n_parts - 1 else self.n_look_ahead - (n_parts - 1) * (self.n_look_ahead // n_parts)
+                eef_xyz_delta = torch.randn((n_sample, eef_xyz.shape[1], eef_xyz.shape[2]), device=self.torch_device, dtype=torch.float32) * self.xyz_noise_level * (1. / (iter_index + 1) ** 1)  # TODO
+                eef_quat_delta = torch.randn((n_sample, eef_quat.shape[1], eef_quat.shape[2]), device=self.torch_device, dtype=torch.float32) * self.quat_noise_level * (1. / (iter_index + 1) ** 1)
+                eef_gripper_delta = torch.randn((n_sample, eef_gripper.shape[1], eef_gripper.shape[2]), device=self.torch_device, dtype=torch.float32) * self.gripper_noise_level * (1. / (iter_index + 1) ** 1)
+                eef_xyz_delta = eef_xyz_delta[:, None].repeat(1, p_len, 1, 1)
+                eef_quat_delta = eef_quat_delta[:, None].repeat(1, p_len, 1, 1)
+                eef_gripper_delta = eef_gripper_delta[:, None].repeat(1, p_len, 1, 1)
+                eef_xyz_delta_list.append(eef_xyz_delta)
+                eef_quat_delta_list.append(eef_quat_delta)
+                eef_gripper_delta_list.append(eef_gripper_delta)
+            eef_xyz_delta = torch.cat(eef_xyz_delta_list, dim=1)
+            eef_quat_delta = torch.cat(eef_quat_delta_list, dim=1)
+            eef_gripper_delta = torch.cat(eef_gripper_delta_list, dim=1)
+
+        eef_xyz_delta_cum = torch.cumsum(eef_xyz_delta, dim=1)  # (n_sample, n_look_ahead, n_grippers, 3)
+        eef_quat_delta_cum = torch.cumsum(eef_quat_delta, dim=1)  # (n_sample, n_look_ahead, n_grippers, 4)
+        eef_gripper_delta_cum = torch.cumsum(eef_gripper_delta, dim=1)  # (n_sample, n_look_ahead, n_grippers, 1)
+
+        eef_xyz = eef_xyz[None] + eef_xyz_delta_cum
+        eef_quat = eef_quat[None] + eef_quat_delta_cum
+        eef_gripper = eef_gripper[None] + eef_gripper_delta_cum
+
+        eef_quat = eef_quat / (torch.norm(eef_quat, dim=-1, keepdim=True) + 1e-6)  # normalize
+        eef_rot = kornia.geometry.conversions.quaternion_to_rotation_matrix(eef_quat)
+
+        act_seqs = torch.zeros((n_sample, self.n_look_ahead, n_grippers * (3 + 9 + 1)), device=self.torch_device, dtype=torch.float32)
+        act_seqs[:, :, :n_grippers * 3] = eef_xyz.reshape(n_sample, self.n_look_ahead, -1)
+        act_seqs[:, :, n_grippers * 3:n_grippers * (3 + 3 * 3)] = eef_rot.reshape(n_sample, self.n_look_ahead, -1)
+        act_seqs[:, :, n_grippers * (3 + 3 * 3):] = eef_gripper.reshape(n_sample, self.n_look_ahead, -1)
+
+        self.clip_actions(act_seqs)
+        return act_seqs
+
+    def evaluate_traj(self, model_out, act_seqs):
+        target_state = self.target_state.clone()
+        x = model_out['x']  # (n_sample, n_look_forward, n_pts, 3)
+        chamfer_distance = batch_chamfer_dist(x[:, -1], target_state)  # (n_sample,)
+        curr_chamfer_distance = batch_chamfer_dist(x[:, 0], target_state)  # (n_sample,)
+        print('curr chamfer_distance:', curr_chamfer_distance.min().item(), end=' ')
+        print('best chamfer_distance:', chamfer_distance.min().item())
+
+        n_sample = self.n_sample_chunk
+        n_grippers = self.cfg.sim.num_grippers
+        assert act_seqs.shape[0] == n_sample
+        eef_xyz = act_seqs[:, :, :n_grippers * 3].reshape(n_sample, self.n_look_ahead, n_grippers, 3)
+
+        if eef_xyz.shape[2] == 2:
+            eef_xyz_left = eef_xyz[:, :, 0]  # (n_sample, self.n_look_ahead, 3)
+            eef_xyz_right = eef_xyz[:, :, 1]  # (n_sample, self.n_look_ahead, 3)
+            eef_xyz_dist = torch.norm(eef_xyz_left - eef_xyz_right, dim=-1)  # (n_sample, self.n_look_ahead)
+            eef_xyz_dist_penalty = (eef_xyz_dist.max(dim=1).values > 0.3).float() * 100.0  # (n_sample,)  # the smaller the better  # TODO distance threshold
+
+            eef_height_penalty = torch.logical_or(eef_xyz_left[:, :, 2].max(dim=1).values > -0.02, eef_xyz_right[:, :, 2].max(dim=1).values > -0.02).to(torch.float32) * 100.0  # (n_sample,)  # the smaller the better
+            eef_height_penalty += torch.logical_or(
+                (eef_xyz_left.max(dim=1).values > (self.bbox[:, 1] - 0.02)).any(dim=-1), 
+                (eef_xyz_left.min(dim=1).values < (self.bbox[:, 0] + 0.02)).any(dim=-1)
+            ).to(torch.float32) * 100.0  # (n_sample,)  # the smaller the better
+            eef_height_penalty += torch.logical_or(
+                (eef_xyz_right.max(dim=1).values > (self.bbox[:, 1] - 0.02)).any(dim=-1), 
+                (eef_xyz_right.min(dim=1).values < (self.bbox[:, 0] + 0.02)).any(dim=-1)
+            ).to(torch.float32) * 100.0  # (n_sample,)  # the smaller the better
+
+            reward = -chamfer_distance - eef_xyz_dist_penalty - eef_height_penalty  # to maximize
+        else:
+            assert eef_xyz.shape[2] == 1
+            eef_xyz_dist_penalty = 0
+            eef_height_penalty = (eef_xyz[:, :, 0, 2].max(dim=1).values > -0.02).to(torch.float32) * 100.0  # (n_sample,)  # the smaller the better
+            eef_height_penalty += torch.logical_or(
+                (eef_xyz[:, :, 0].max(dim=1).values > (self.bbox[:, 1] - 0.02)).any(dim=-1), 
+                (eef_xyz[:, :, 0].min(dim=1).values < (self.bbox[:, 0] + 0.02)).any(dim=-1)
+            ).to(torch.float32) * 100.0  # (n_sample,)  # the smaller the better
+            reward = -chamfer_distance - eef_xyz_dist_penalty - eef_height_penalty  # to maximize
+
+        print('best reward:', reward.max().item())
+        eval_out = {
+            'reward_seqs': reward,
+        }
+        return eval_out
+
+    def optimize_action_mppi(self, act_seqs, reward_seqs):
+
+        weight_seqs = F.softmax(reward_seqs * self.reward_weight, dim=0)  # (n_sample,)
+        assert len(weight_seqs.shape) == 1 and weight_seqs.shape[0] == self.n_sample_chunk
+
+        n_sample = self.n_sample_chunk
+        n_grippers = self.cfg.sim.num_grippers
+        assert act_seqs.shape[0] == n_sample
+        eef_xyz = act_seqs[:, :, :n_grippers * 3].reshape(n_sample, self.n_look_ahead, n_grippers, 3)
+        eef_rot = act_seqs[:, :, n_grippers * 3:n_grippers * (3 + 3 * 3)].reshape(n_sample, self.n_look_ahead, n_grippers, 3, 3)
+        eef_quat = kornia.geometry.conversions.rotation_matrix_to_quaternion(eef_rot)
+        eef_gripper = act_seqs[:, :, n_grippers * (3 + 3 * 3):].reshape(n_sample, self.n_look_ahead, n_grippers, 1)
+
+        eef_xyz = torch.sum(weight_seqs[:, None, None, None] * eef_xyz, dim=0)  # (n_look_ahead, n_grippers, 3)
+        eef_gripper = torch.sum(weight_seqs[:, None, None, None] * eef_gripper, dim=0)  # (n_look_ahead, n_grippers, 1)
+        eef_quat = torch.sum(weight_seqs[:, None, None, None] * eef_quat, dim=0)  # (n_look_ahead, n_grippers, 4)
+
+        eef_quat = eef_quat / (torch.norm(eef_quat, dim=-1, keepdim=True) + 1e-6)  # normalize
+        
+        eef_rot = kornia.geometry.conversions.quaternion_to_rotation_matrix(eef_quat)
+        act_seq = torch.zeros((self.n_look_ahead, n_grippers * (3 + 9 + 1)), device=self.torch_device, dtype=torch.float32)
+        act_seq[:, :n_grippers * 3] = eef_xyz.reshape(self.n_look_ahead, -1)
+        act_seq[:, n_grippers * 3:n_grippers * (3 + 3 * 3)] = eef_rot.reshape(self.n_look_ahead, -1)
+        act_seq[:, n_grippers * (3 + 3 * 3):] = eef_gripper.reshape(self.n_look_ahead, -1)
+
+        act_seq = self.clip_actions(act_seq[None])[0]
+        return act_seq
+
+    def clip_actions(self, act_seqs):
+        no_sample_dim = False
+        if len(act_seqs.shape) == 2:
+            no_sample_dim = True
+            act_seqs = act_seqs[None]
+        n_sample = act_seqs.shape[0]
+        n_grippers = self.cfg.sim.num_grippers
+        eef_xyz = act_seqs[:, :, :n_grippers * 3].reshape(n_sample, self.n_look_ahead, n_grippers, 3)
+        eef_rot = act_seqs[:, :, n_grippers * 3:n_grippers * (3 + 3 * 3)].reshape(n_sample, self.n_look_ahead, n_grippers, 3, 3)
+        eef_quat = kornia.geometry.conversions.rotation_matrix_to_quaternion(eef_rot)  # (n_sample, n_look_ahead, n_grippers, 4)
+        eef_gripper = act_seqs[:, :, n_grippers * (3 + 3 * 3):].reshape(n_sample, self.n_look_ahead, n_grippers, 1)
+
+        eef_xyz = torch.clamp(eef_xyz, self.bbox[:, 0], self.bbox[:, 1])
+        eef_aa = kornia.geometry.conversions.quaternion_to_axis_angle(eef_quat)  # (n_sample, n_look_ahead, n_grippers, 3)
+        max_rad = 1.0
+        eef_aa_mask = torch.norm(eef_aa, dim=-1) > max_rad  # (n_sample, n_look_ahead, n_grippers)
+        eef_aa[eef_aa_mask] = eef_aa[eef_aa_mask] / torch.norm(eef_aa[eef_aa_mask], dim=-1, keepdim=True) * max_rad  # cannot exceed 0.5 rad
+        eef_quat = kornia.geometry.conversions.axis_angle_to_quaternion(eef_aa)
+        eef_quat = eef_quat / (torch.norm(eef_quat, dim=-1, keepdim=True) + 1e-6)  # normalize
+        eef_gripper = torch.clamp(eef_gripper, 0.0, 1.0)
+
+        eef_rot = kornia.geometry.conversions.quaternion_to_rotation_matrix(eef_quat)
+        act_seqs[:, :, :n_grippers * 3] = eef_xyz.reshape(n_sample, self.n_look_ahead, -1)
+        act_seqs[:, :, n_grippers * 3:n_grippers * (3 + 3 * 3)] = eef_rot.reshape(n_sample, self.n_look_ahead, -1)
+        act_seqs[:, :, n_grippers * (3 + 3 * 3):] = eef_gripper.reshape(n_sample, self.n_look_ahead, -1)
+        if no_sample_dim:
+            assert act_seqs.shape[0] == 1
+            act_seqs = act_seqs[0]
+        return act_seqs
+
+
+    def get_command(self, state, save_dir=None, is_first_iter=False):
+
+        self.state = state
+
+        best_act_seq = None
+        best_reward_seq = None
+
+        pts = state["perception_out"]["value"]["pts"].copy()
+        pts = np.concatenate(pts, axis=0)
+
+        # remove outliers
+        rm_outliers = False
+        if rm_outliers:
+            pcd_rm = o3d.geometry.PointCloud()
+            pcd_rm.points = o3d.utility.Vector3dVector(pts)
+            outliers = None
+            new_outlier = None
+            rm_iter = 0
+            while new_outlier is None or len(new_outlier.points) > 0:
+                _, inlier_idx = pcd_rm.remove_statistical_outlier(
+                    nb_neighbors = 30, std_ratio = 2.5 + rm_iter * 0.5
+                )
+                new_pcd = pcd_rm.select_by_index(inlier_idx)
+                new_outlier = pcd_rm.select_by_index(inlier_idx, invert=True)
+                if outliers is None:
+                    outliers = new_outlier
+                else:
+                    outliers += new_outlier
+                pcd_rm = new_pcd
+                rm_iter += 1
+            pts = np.array(pcd_rm.points)
+
+        pts = torch.tensor(pts, dtype=torch.float32, device=self.torch_device)
+
+        if is_first_iter:
+            self.dynamics_module.reset_preprocess_meta(pts)
+        self.dynamics_module.reset_downsample_indices(pts)
+
+        self.pts = pts
+
+        if save_dir is not None:
+            os.makedirs(save_dir, exist_ok=True)
+
+            chamfer_now = batch_chamfer_dist(pts[None], self.target_state.clone())
+            print('chamfer_now:', chamfer_now.item())
+            with open(Path(save_dir) / 'chamfer.txt', 'w') as f:
+                f.write(str(chamfer_now.item()))
+
+            pts_save = pts.cpu().numpy()
+            o3d_pts = o3d.geometry.PointCloud()
+            o3d_pts.points = o3d.utility.Vector3dVector(pts_save)
+            o3d.io.write_point_cloud(str(Path(save_dir) / 'pts.ply'), o3d_pts)
+
+            target_state_save = self.target_state.cpu().numpy()
+            o3d_target_state = o3d.geometry.PointCloud()
+            o3d_target_state.points = o3d.utility.Vector3dVector(target_state_save)
+            o3d.io.write_point_cloud(str(Path(save_dir) / 'target_state.ply'), o3d_target_state)
+
+        if self.bimanual:
+            left_robot_out = state["robot_out"]["left_value"]
+            left_gripper_out = state["gripper_out"]["left_value"]
+            right_robot_out = state["robot_out"]["right_value"]
+            right_gripper_out = state["gripper_out"]["right_value"]
+            left_robot_out = torch.tensor(left_robot_out, dtype=torch.float32, device=self.torch_device)
+            left_gripper_out = torch.tensor(left_gripper_out, dtype=torch.float32, device=self.torch_device)
+            right_robot_out = torch.tensor(right_robot_out, dtype=torch.float32, device=self.torch_device)
+            right_gripper_out = torch.tensor(right_gripper_out, dtype=torch.float32, device=self.torch_device)
+            robot_out = None
+            gripper_out = None
+        else:
+            robot_out = state["robot_out"]["value"]
+            gripper_out = state["gripper_out"]["value"]
+            robot_out = torch.tensor(robot_out, dtype=torch.float32, device=self.torch_device)
+            gripper_out = torch.tensor(gripper_out, dtype=torch.float32, device=self.torch_device)
+            left_robot_out = None
+            left_gripper_out = None
+            right_robot_out = None
+            right_gripper_out = None
+        
+        if self.bimanual:
+            b2w_l = state["b2w_l"]
+            b2w_r = state["b2w_r"]
+            b2w_l = torch.tensor(b2w_l, dtype=torch.float32, device=self.torch_device)
+            b2w_r = torch.tensor(b2w_r, dtype=torch.float32, device=self.torch_device)
+            b2w = None
+        else:
+            b2w = state["b2w"]
+            b2w = torch.tensor(b2w, dtype=torch.float32, device=self.torch_device)
+            b2w_l = None
+            b2w_r = None
+
+        # construct act_seq using current robot state
+        # assert not self.cfg.sim.gripper_points
+        eef_xyz = torch.zeros((self.n_look_ahead + 1, self.cfg.sim.num_grippers, 3), device=self.torch_device)
+        eef_quat = torch.zeros((self.n_look_ahead + 1, self.cfg.sim.num_grippers, 4), device=self.torch_device)
+        eef_quat[:, :, 0] = 1.0
+        eef_gripper = torch.zeros((self.n_look_ahead + 1, 1), device=self.torch_device)
+        
+        # construct eef_world
+        eef_points = torch.tensor([[0.0, 0.0, 0.175, 1]], device=self.torch_device)  # the eef point in the gripper frame
+        eef_axis = torch.tensor([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0]], dtype=torch.float32, device=self.torch_device)  # (3, 4)
+        if self.bimanual:
+            left_eef_world_list = []
+            right_eef_world_list = []
+            assert left_robot_out is not None and right_robot_out is not None
+            assert b2w_l is not None and b2w_r is not None
+            for e2b, b2w, eef_world_list in zip([left_robot_out, right_robot_out], [b2w_l, b2w_r], [left_eef_world_list, right_eef_world_list]):
+                eef_points_world = (b2w @ e2b @ eef_points.T).T[:, :3]  # (n, 3)
+                # eef_points_vis.append(eef_points)
+                # eef_points_world_vis.append(eef_points_world)
+                eef_orientation_world = (b2w[:3, :3] @ e2b[:3, :3] @ eef_axis[:, :3].T).T  # (3, 3)
+                eef_world = torch.cat([eef_points_world, eef_orientation_world], dim=0)  # (n+3, 3)
+                eef_world_list.append(eef_world)
+            left_eef_world = torch.cat(left_eef_world_list, dim=0)  # (n+3, 3)
+            right_eef_world = torch.cat(right_eef_world_list, dim=0)  # (n+3, 3)
+            eef_world = torch.cat([left_eef_world, right_eef_world], dim=0)  # (2n+6, 3)
+        else:
+            assert robot_out is not None
+            assert b2w is not None
+            e2b = robot_out  # (4, 4)
+            eef_points_world = (b2w @ e2b @ eef_points.T).T[:, :3]  # (n, 3)
+            # eef_points_vis.append(eef_points)
+            # eef_points_world_vis.append(eef_points_world)
+            eef_orientation_world = (b2w[:3, :3] @ e2b[:3, :3] @ eef_axis[:, :3].T).T  # (3, 3)
+            eef_world = torch.cat([eef_points_world, eef_orientation_world], dim=0)  # (n+3, 3)
+        
+        if self.bimanual:
+            assert left_gripper_out is not None and right_gripper_out is not None
+            gripper_world = torch.tensor([left_gripper_out, right_gripper_out, 0.0], device=self.torch_device)[None, :]  # (1, 3)
+        else:
+            assert gripper_out is not None
+            gripper_world = torch.tensor([gripper_out, 0.0, 0.0], device=self.torch_device)[None, :]  # (1, 3)
+        
+        eef_world = torch.cat([eef_world, gripper_world], dim=0)  # (n+4, 3) or (2n+7, 3)
+        robot = eef_world
+
+        # decode eef_world
+        if len(robot.shape) > 1:  # 5 or 9
+            assert robot.shape[0] in [5, 9]  # bi-manual (2 * (1 pos + 3 rot) + 1 gripper) or single arm (1 pos + 3 rot + 1 gripper or 1 pos)
+            gripper = robot[-1]
+            robot = robot[:-1]
+            robot = robot.reshape(-1, 4, 3)
+            robot_trans = robot[:, 0]  # (n, 3)
+            robot_rot = robot[:, 1:]  # (n, 3, 3)
+            if robot_trans.shape[0] == 1:  # single arm
+                gripper = gripper[:1]  # (1,)
+            else:  # bi-manual
+                gripper = gripper[:2]  # (2,)
+        else:
+            assert len(robot.shape) == 1 and robot.shape[0] == 3
+            robot_trans = robot
+            robot_rot = torch.eye(3).to(self.torch_device).to(torch.float32)
+            gripper = torch.tensor([0.0], device=self.torch_device).to(torch.float32)
+        robot_trans = robot_trans #  + torch.tensor([0, 0, -0.01], device=self.torch_device)  # offset
+        gripper = torch.clamp(gripper / 800.0, 0, 1)  # 1: open, 0: close
+
+        # init eef variables in world frame
+        eef_xyz = robot_trans.reshape(-1, 3)
+        eef_rot = robot_rot.reshape(-1, 3, 3)  # (n_grippers, 3, 3)
+        eef_gripper = gripper.reshape(-1)  # (n_grippers,)
+        assert eef_xyz.shape[0] == eef_rot.shape[0] == eef_gripper.shape[0]
+        n_grippers = eef_xyz.shape[0]
+
+        self.eef_xyz = eef_xyz
+        self.eef_rot = eef_rot
+        self.eef_gripper = eef_gripper
+
+        # init act_seq in world frame
+        act_seq = torch.zeros((self.n_look_ahead, n_grippers * (3 + 9 + 1)), device=self.torch_device)
+        act_seq[:, :n_grippers * 3] = eef_xyz.reshape(-1)
+        act_seq[:, n_grippers * 3:n_grippers * (3 + 3 * 3)] = eef_rot.reshape(-1)
+        act_seq[:, n_grippers * (3 + 3 * 3):] = eef_gripper.reshape(-1)
+
+        res_all = []
+        for ci in range(self.n_chunk):
+
+            best_act_seq = act_seq
+            for ti in range(self.n_update_iter):
+
+                print(f'chunk: {ci}/{self.n_chunk}, iter: {ti}/{self.n_update_iter}')
+                
+                with torch.no_grad():
+                    act_seqs = self.sample_action_seq(act_seq, iter_index=ti)  # support iteration-dependent noise
+                    model_out = self.model_rollout(act_seqs, visualize_pv=False)  # TODO
+                    eval_out = self.evaluate_traj(model_out, act_seqs)
+                    reward_seqs = eval_out["reward_seqs"]  # (n_sample,)
+                    act_seq = self.optimize_action_mppi(act_seqs, reward_seqs)
+
+                    best_reward_idx = torch.argmax(reward_seqs)
+
+                    if ti == 0:
+                        best_act_seq = act_seqs[best_reward_idx]
+                        best_reward_seq = reward_seqs[best_reward_idx]
+                    elif reward_seqs[best_reward_idx] > best_reward_seq:
+                        best_act_seq = act_seqs[best_reward_idx]
+                        best_reward_seq = reward_seqs[best_reward_idx]
+                    
+                    # model_out = self.model_rollout(best_act_seq[None].repeat(self.n_sample_chunk, 1, 1), visualize_pv=True)  # TODO
+
+            torch.cuda.empty_cache()
+            # model_out = self.model_rollout(best_act_seq[None].repeat(self.n_sample_chunk, 1, 1), visualize_pv=True)  # TODO
+
+            res = {
+                "best_act_seq": best_act_seq,  # (n_look_ahead, n_grippers * (3 + 9 + 1))
+                "best_reward_seq": best_reward_seq,
+            }
+            res_all.append(res)
+
+        reward_list = [res["best_reward_seq"].item() for res in res_all]
+        best_idx = np.argmax(reward_list)
+        best_act_seq = res_all[best_idx]['best_act_seq']  # (n_look_ahead, n_grippers * (3 + 9 + 1))
+
+        torch.cuda.empty_cache()
+
+        eef_xyz = best_act_seq[:, :n_grippers * 3].reshape(self.n_look_ahead, n_grippers, 3)
+        eef_rot = best_act_seq[:, n_grippers * 3:n_grippers * (3 + 3 * 3)].reshape(self.n_look_ahead, n_grippers, 3, 3)
+        eef_gripper = best_act_seq[:, n_grippers * (3 + 3 * 3):].reshape(self.n_look_ahead, n_grippers, 1)
+
+        # transform back into robot coordinates
+        eef_points = torch.tensor([[0.0, 0.0, 0.175, 1]], device=self.torch_device)  # the eef point in the gripper frame
+        eef_axis = torch.tensor([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0]], dtype=torch.float32, device=self.torch_device)  # (3, 4)
+
+        # initialize command
+        command = [[] for _ in range(eef_xyz.shape[-2])]  # (n_grippers,)
+        e2b_command = [[] for _ in range(eef_xyz.shape[-2])]  # (n_grippers,)
+
+        look_ahead_range = range(self.n_look_ahead)
+
+        for li in look_ahead_range:
+            
+            if self.bimanual:
+                assert b2w_l is not None and b2w_r is not None
+
+                left_eef_xyz = eef_xyz[li:li+1, 0]  # (1, 3)
+                right_eef_xyz = eef_xyz[li:li+1, 1]  # (1, 3)
+
+                left_eef_rot = eef_rot[li, 0]  # (3, 3)
+                right_eef_rot = eef_rot[li, 1]  # (3, 3)
+
+                e2b_l = torch.eye(4, device=self.torch_device)
+                e2b_r = torch.eye(4, device=self.torch_device)
+
+                for b2w, e2b, eef_points_world, eef_orientation_world in \
+                    zip([b2w_l, b2w_r], [e2b_l, e2b_r], [left_eef_xyz, right_eef_xyz], [left_eef_rot, right_eef_rot]):
+
+                    eef_orientation_world = eef_orientation_world.T  # (3, 3) = b2w[:3, :3] @ e2b[:3, :3] @ eef_axis[:, :3].T
+                    eef_orientation_world = b2w[:3, :3].T @ eef_orientation_world  # (3, 3) = e2b[:3, :3] @ eef_axis[:, :3].T
+                    e2b[:3, :3] = eef_orientation_world @ eef_axis[:, :3]  # (3, 3) = e2b[:3, :3]
+
+                    eef_points_world = eef_points_world.T  # (3, n) = b2w_R @ (e2b_R @ eef_points[:, :3].T + e2b_t) + b2w_t
+                    eef_points_world = eef_points_world - b2w[:3, 3].reshape(-1, 1)  # (3, n) = b2w_R @ (e2b_R @ eef_points[:, :3].T + e2b_t)
+                    eef_points_world = b2w[:3, :3].T @ eef_points_world  # (3, n) = e2b_R @ eef_points[:, :3].T + e2b_t
+                    e2b[:3, 3:4] = eef_points_world - e2b[:3, :3] @ eef_points[:, :3].T  # (3, n) = e2b_t
+
+                e2b_list = [e2b_l, e2b_r]
+
+            else:
+                assert b2w is not None
+
+                eef_points_world = eef_xyz[li:li+1, 0]  # (n, 3)
+                eef_orientation_world = eef_rot[li, 0]
+
+                e2b = torch.eye(4, device=self.torch_device)
+
+                eef_orientation_world = eef_orientation_world.T  # (3, 3) = b2w[:3, :3] @ e2b[:3, :3] @ eef_axis[:, :3].T
+                eef_orientation_world = b2w[:3, :3].T @ eef_orientation_world  # (3, 3) = e2b[:3, :3] @ eef_axis[:, :3].T
+                e2b[:3, :3] = eef_orientation_world @ eef_axis[:, :3]  # (3, 3) = e2b[:3, :3]
+
+                eef_points_world = eef_points_world.T  # (3, n) = b2w_R @ (e2b_R @ eef_points[:, :3].T + e2b_t) + b2w_t
+                eef_points_world = eef_points_world - b2w[:3, 3].reshape(-1, 1)  # (3, n) = b2w_R @ (e2b_R @ eef_points[:, :3].T + e2b_t)
+                eef_points_world = b2w[:3, :3].T @ eef_points_world  # (3, n) = e2b_R @ eef_points[:, :3].T + e2b_t
+                e2b[:3, 3:4] = eef_points_world - e2b[:3, :3] @ eef_points[:, :3].T  # (3, n) = e2b_t
+
+                e2b_list = [e2b]
+
+
+            for gripper_id in range(eef_xyz.shape[-2]):
+
+                fk_trans_mat = e2b_list[gripper_id].cpu().numpy()
+
+                cur_xyzrpy = np.zeros(6)
+                cur_xyzrpy[:3] = fk_trans_mat[:3, 3] * 1000
+                cur_xyzrpy[3:] = transforms3d.euler.mat2euler(fk_trans_mat[:3, :3])
+                cur_xyzrpy[3:] = cur_xyzrpy[3:] / np.pi * 180
+
+                gripper = eef_gripper[li, gripper_id].item()
+                gripper = gripper * 800.0
+
+                single_command = list(cur_xyzrpy) + [gripper]
+                command[gripper_id].append(single_command)
+
+                # debug
+                e2b_command[gripper_id].append(e2b_list[gripper_id].cpu().numpy())
+
+        return command
diff --git a/src/experiments/real_world/modules_planning/segment_perception.py b/src/experiments/real_world/modules_planning/segment_perception.py
new file mode 100644
index 0000000000000000000000000000000000000000..b275738b31f2b914ccac5b62ad68a4d18737a983
--- /dev/null
+++ b/src/experiments/real_world/modules_planning/segment_perception.py
@@ -0,0 +1,310 @@
+from pathlib import Path
+import os
+import time
+import numpy as np
+import torch
+import cv2
+import open3d as o3d
+from threadpoolctl import threadpool_limits
+import multiprocess as mp
+from functools import partial
+from PIL import Image
+import supervision as sv
+
+from pgnd.utils import get_root
+root: Path = get_root(__file__)
+
+from camera.multi_realsense import MultiRealsense
+from camera.single_realsense import SingleRealsense
+
+from transformers import AutoProcessor, AutoModelForZeroShotObjectDetection 
+from sam2.build_sam import build_sam2, build_sam2_video_predictor
+from sam2.sam2_image_predictor import SAM2ImagePredictor
+
+from utils.pcd_utils import depth2fgpcd
+
+
+def get_mask_raw(depth, intr, extr, bbox, depth_threshold=[0, 2]):
+    points = depth2fgpcd(depth, intr).reshape(-1, 3)
+    mask = np.logical_and((depth > depth_threshold[0]), (depth < depth_threshold[1]))  # (H, W)
+
+    points = (np.linalg.inv(extr) @ np.concatenate([points, np.ones((points.shape[0], 1)).astype(np.float32)], axis=1).T).T[:, :3]  # (N, 3)
+    mask_bbox = np.logical_and(
+        np.logical_and(points[:, 0] > bbox[0][0], points[:, 0] < bbox[0][1]),
+        np.logical_and(points[:, 1] > bbox[1][0], points[:, 1] < bbox[1][1])
+    )  # does not include z axis
+    mask_bbox = mask_bbox.reshape(depth.shape[0], depth.shape[1])
+    mask = np.logical_and(mask, mask_bbox)
+    return mask
+
+
+def segment_process_func(cameras_output, intrs, extrs, text_prompts, processor, grounding_model, image_predictor, bbox, device, show_annotation=True):
+    colors_list = []
+    depths_list = []
+    pts_list = []
+    for ck, cv in cameras_output.items():
+
+        image = cv["color"].copy()
+        depth = cv["depth"].copy() / 1000.0
+        image = Image.fromarray(image)
+
+        # ground
+        inputs = processor(images=image, text=text_prompts, return_tensors="pt").to(device)
+        with torch.no_grad():
+            outputs = grounding_model(**inputs)
+        results = processor.post_process_grounded_object_detection(
+            outputs,
+            inputs.input_ids,
+            box_threshold=0.325,
+            text_threshold=0.3,
+            target_sizes=[image.size[::-1]]
+        )
+        input_boxes = results[0]["boxes"].cpu().numpy()
+        objects = results[0]["labels"]
+
+        depth_mask = get_mask_raw(depth, intrs[ck], extrs[ck], bbox)
+
+        multi_objs = False
+        if len(objects) > 1:
+            objects_masked = []
+            input_boxes_masked = []
+            if intrs is None or extrs is None:
+                print("No camera intrinsics and extrinsics provided")
+                return {
+                    "color": [],
+                    "depth": [],
+                    "pts": [],
+                }
+            for i, obj in enumerate(objects):
+                if obj == '':
+                    continue
+                box = input_boxes[i].astype(int)
+                if (box[3] - box[1]) * (box[2] - box[0]) > 500 * 400:
+                    continue
+                depth_mask_box = depth_mask[box[1]:box[3], box[0]:box[2]]
+                if depth_mask_box.sum() > 0:
+                    objects_masked.append(obj)
+                    input_boxes_masked.append(box)
+            objects = objects_masked
+            input_boxes = input_boxes_masked
+            if len(objects) == 0:
+                print("No objects detected")
+                return {
+                    "color": [],
+                    "depth": [],
+                    "pts": [],
+                }
+            elif len(objects) > 1:
+                multi_objs = True
+
+        image_predictor.set_image(np.array(image.convert("RGB")))
+        masks, scores, logits = image_predictor.predict(
+            point_coords=None,
+            point_labels=None,
+            box=input_boxes,
+            multimask_output=False,
+        )
+        if masks.ndim == 3:
+            pass
+        elif masks.ndim == 4:
+            assert multi_objs
+            masks = masks.squeeze(1)
+        masks = masks.astype(bool)
+
+        ID_TO_OBJECTS = {i: obj for i, obj in enumerate(objects, start=1)}
+        object_ids = np.arange(1, len(objects) + 1)
+
+        detections = sv.Detections(
+            xyxy=sv.mask_to_xyxy(masks),  # (n, 4)
+            mask=masks, # (n, h, w)
+            class_id=np.array(object_ids, dtype=np.int32),
+        )
+        box_annotator = sv.BoxAnnotator()
+        if show_annotation:
+            annotated_frame = box_annotator.annotate(scene=np.array(image).astype(np.uint8), detections=detections)
+            label_annotator = sv.LabelAnnotator()
+            annotated_frame = label_annotator.annotate(annotated_frame, detections=detections, labels=[ID_TO_OBJECTS[i] for i in object_ids])
+            mask_annotator = sv.MaskAnnotator()
+            annotated_frame = mask_annotator.annotate(scene=annotated_frame, detections=detections)
+            colors_list.append(annotated_frame)
+        else:
+            colors_list.append(np.array(image))
+
+        depths_list.append(cv["depth"].copy())
+
+        masks = np.logical_or.reduce(masks, axis=0, keepdims=True)
+        masks = np.logical_and(masks, depth_mask)
+        masks = masks.reshape(-1)
+        assert masks.shape[0] == depth.shape[0] * depth.shape[1]
+        points = depth2fgpcd(depth, intrs[ck]).reshape(-1, 3)
+        points = (np.linalg.inv(extrs[ck]) @ np.concatenate([points, np.ones((points.shape[0], 1)).astype(np.float32)], axis=1).T).T[:, :3]  # (N, 3)
+        points = points[masks]
+        pts_list.append(points)
+
+    return {
+        "color": colors_list,
+        "depth": depths_list,
+        "pts": pts_list,
+    }
+
+
+class SegmentPerception(mp.Process):
+
+    def __init__(
+        self,
+        realsense: MultiRealsense | SingleRealsense, 
+        capture_fps, 
+        record_fps, 
+        record_time,
+        exp_name=None,
+        bbox=None,
+        data_dir="data",
+        text_prompts="white cotton rope.",
+        show_annotation=True,
+        device=None,
+        verbose=False,
+    ):
+        super().__init__()
+        self.verbose = verbose
+
+        self.capture_fps = capture_fps
+        self.record_fps = record_fps
+        self.record_time = record_time
+        self.exp_name = exp_name
+        self.data_dir = data_dir
+        self.bbox = bbox
+
+        self.text_prompts = text_prompts
+        self.show_annotation = show_annotation
+
+        if self.exp_name is None:
+            assert self.record_fps == 0
+
+        self.realsense = realsense
+        self.perception_q = mp.Queue(maxsize=1)
+
+        self.num_cam = len(realsense.cameras.keys())
+        self.alive = mp.Value('b', False)
+        self.record_restart = mp.Value('b', False)
+        self.record_stop = mp.Value('b', False)
+        self.do_process = mp.Value('b', True)
+
+        self.intrs = mp.Array('d', [0.0] * 9 * self.num_cam)
+        self.extrs = mp.Array('d', [0.0] * 16 * self.num_cam)
+
+    def log(self, msg):
+        if self.verbose:
+            print(f"\033[92m{self.name}: {msg}\033[0m")
+
+    @property
+    def can_record(self):
+        return self.record_fps != 0
+    
+    def update_intrinsics(self, intrs):
+        self.intrs[:] = intrs.flatten()
+    
+    def update_extrinsics(self, extrs):
+        self.extrs[:] = extrs.flatten()
+
+    def run(self):
+        # limit threads
+        threadpool_limits(1)
+        cv2.setNumThreads(1)
+
+        realsense = self.realsense
+
+        # i = self.index
+        capture_fps = self.capture_fps
+        record_fps = self.record_fps
+        record_time = self.record_time
+
+        cameras_output = None
+        recording_frame = float("inf")  # local record step index (since current record start), record fps
+        record_start_frame = 0  # global step index (since process start), capture fps
+        is_recording = False  # recording state flag
+        timestamps_f = None
+
+        checkpoint = str(root.parent / "weights/sam2/sam2.1_hiera_large.pt")
+        model_cfg = "configs/sam2.1/sam2.1_hiera_l.yaml"
+        model_id = "IDEA-Research/grounding-dino-tiny"
+        device = "cuda" if torch.cuda.is_available() else "cpu"
+        processor = AutoProcessor.from_pretrained(model_id)
+        grounding_model = AutoModelForZeroShotObjectDetection.from_pretrained(model_id).to(device)
+        image_predictor = SAM2ImagePredictor(build_sam2(model_cfg, checkpoint))
+        
+        process_func = partial(
+            segment_process_func,
+            text_prompts=self.text_prompts,
+            processor=processor,
+            grounding_model=grounding_model,
+            image_predictor=image_predictor,
+            bbox=self.bbox,
+            device=device,
+            show_annotation=self.show_annotation,
+        )
+
+        while self.alive.value:
+            try: 
+                if not self.do_process.value:
+                    if not self.perception_q.empty():
+                        self.perception_q.get()
+                    time.sleep(1)
+                    continue
+                cameras_output = realsense.get(out=cameras_output)
+                get_time = time.time()
+                timestamps = [cameras_output[i]['timestamp'].item() for i in range(self.num_cam)]  # type: ignore
+                if is_recording and not all([abs(timestamps[i] - timestamps[i+1]) < 0.05 for i in range(self.num_cam - 1)]):
+                    print(f"Captured at different timestamps: {[f'{x:.2f}' for x in timestamps]}")
+
+                # treat captured time and record time as the same
+                process_start_time = get_time
+
+                intrs = np.frombuffer(self.intrs.get_obj()).reshape((self.num_cam, 3, 3))
+                extrs = np.frombuffer(self.extrs.get_obj()).reshape((self.num_cam, 4, 4))
+
+                if intrs.sum() == 0 or extrs.sum() == 0:
+                    print("No camera intrinsics and extrinsics provided")
+                    time.sleep(1)
+                    continue
+
+                process_out = process_func(cameras_output, intrs, extrs)
+                self.log(f"process time: {time.time() - process_start_time}")
+            
+                if not self.perception_q.full():
+                    self.perception_q.put(process_out)
+
+            except BaseException as e:
+                print("Perception error: ", e.with_traceback())
+                break
+
+        if self.can_record:
+            if timestamps_f is not None and not timestamps_f.closed:
+                timestamps_f.close()
+            finish_time = time.time()
+        self.stop()
+        print("Perception process stopped")
+
+
+    def start(self):
+        self.alive.value = True
+        super().start()
+
+    def stop(self):
+        self.alive.value = False
+        self.perception_q.close()
+    
+    def set_record_start(self):
+        if self.record_fps == 0:
+            print("record disabled because record_fps is 0")
+            assert self.record_restart.value == False
+        else:
+            self.record_restart.value = True
+            print("record restart cmd received")
+
+    def set_record_stop(self):
+        if self.record_fps == 0:
+            print("record disabled because record_fps is 0")
+            assert self.record_stop.value == False
+        else:
+            self.record_stop.value = True
+            print("record stop cmd received")
diff --git a/src/experiments/real_world/modules_planning/udp_util.py b/src/experiments/real_world/modules_planning/udp_util.py
new file mode 100644
index 0000000000000000000000000000000000000000..f601a5af5c029a994dedcefb7454e4b655fbc086
--- /dev/null
+++ b/src/experiments/real_world/modules_planning/udp_util.py
@@ -0,0 +1,104 @@
+import socket
+import numpy as np
+import pickle
+import threading
+import time
+
+from modules_planning.common.communication import *
+
+np.set_printoptions(precision=3, suppress=True)
+
+class udpSender:
+    def __init__(self, port: dict):
+        self.port = port
+        self.sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
+        self.sock.setsockopt(socket.SOL_SOCKET, socket.SO_BROADCAST, 1)
+
+    def send(self, data: dict):
+        self.sock.sendto(pickle.dumps(data), ('127.0.0.1', self.port))
+
+    def close(self):
+        self.sock.close()
+
+class udpReceiver:
+    def __init__(self, ports: dict, re_use_address = False):
+        self.ports = ports
+        self.re_use_address = re_use_address
+        self.results = {}
+        self.lock = threading.Lock()
+        self.thread = None
+
+    def receive(self, name, port):
+        sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
+        if self.re_use_address:
+            sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
+        sock.bind(('127.0.0.1', port))
+        # sock.settimeout(0.1)  # Set timeout to 1 second
+        while self.alive:
+            try:
+                t0 = time.time()
+                try:
+                    data, addr = sock.recvfrom(32768)
+                except socket.timeout:
+                    continue
+                data = pickle.loads(data)
+                with self.lock:
+                    self.results[name] = data
+                t1 = time.time()
+                # print(f"Receive {name} data from {addr} in {t1-t0:.6f} seconds")
+            except BaseException as e:
+                print(f"Error in receiving {name} data: {e}")
+                break
+        sock.close()
+        print(f"Receive {name} thread stopped")
+
+    def start(self):
+        self.alive = True
+        self.thread = []
+        for name, port in self.ports.items():
+            t = threading.Thread(target=self.receive, args=(name, port))
+            t.start()
+            self.thread.append(t)
+
+    def stop(self):
+        print("Stopping udpReceiver")
+        self.alive = False
+        for t in self.thread:
+            if t is not None and t.is_alive():
+                t.join()
+        print("udpReceiver stopped")
+
+    def get(self, name=None, pop=False):
+        with self.lock:
+            if name is None:
+                ret = self.results
+                if pop:
+                    self.results = {}
+                return ret
+            else:
+                if name in self.results:
+                    if pop:
+                        ret = self.results[name]
+                        del self.results[name]
+                        return ret
+                else:
+                    return None
+
+if __name__ == '__main__':
+    udpReceiver = udpReceiver(ports=
+                            {
+                            'object_pose': OBJECT_POSE_PORT,
+                            'xarm_state': XARM_STATE_PORT
+                            })
+    udpReceiver.start()
+    try:
+        while True:
+            print(udpReceiver.get())
+            print()
+            time.sleep(0.01)
+    except KeyboardInterrupt:
+        try:
+            udpReceiver.lock.release()
+        except:
+            pass
+        udpReceiver.stop()
diff --git a/src/experiments/real_world/modules_planning/xarm_controller.py b/src/experiments/real_world/modules_planning/xarm_controller.py
new file mode 100644
index 0000000000000000000000000000000000000000..4a8d14bcfba66be4a9f656785ccc7dd3fae5c67f
--- /dev/null
+++ b/src/experiments/real_world/modules_planning/xarm_controller.py
@@ -0,0 +1,571 @@
+import threading
+import multiprocess as mp
+import time
+from enum import Enum
+import traceback
+
+import numpy as np
+import transforms3d
+import copy
+
+from xarm.wrapper import XArmAPI
+
+from .kinematics_utils import KinHelper
+from .udp_util import udpReceiver, udpSender
+from modules_planning.common.communication import XARM_STATE_PORT, XARM_CONTROL_PORT, XARM_CONTROL_PORT_L, XARM_CONTROL_PORT_R
+from modules_planning.common.xarm import *
+
+np.set_printoptions(precision=2, suppress=True)
+
+
+class Rate:
+    def __init__(self, *, duration):
+        self.duration = duration
+        self.last = time.time()
+
+    def sleep(self, duration=None) -> None:
+        duration = self.duration if duration is None else duration
+        assert duration >= 0
+        now = time.time()
+        passed = now - self.last
+        remaining = duration - passed
+        assert passed >= 0
+        if remaining > 0.0001:
+            time.sleep(remaining)
+        self.last = time.time()
+
+
+class ControllerState(Enum):
+    INIT = 0
+    RUNNING = 1
+    STOP = 2
+
+class XarmController(mp.Process):
+    
+    name = "xarm_controller"
+    kin_helper = KinHelper(robot_name='xarm7')
+
+    XY_MIN, XY_MAX = XY_MIN, XY_MAX 
+    X_MIN, X_MAX = X_MIN, X_MAX 
+    Y_MIN, Y_MAX = Y_MIN, Y_MAX 
+    Z_MIN, Z_MAX = Z_MIN, Z_MAX 
+    
+    MOVE_RATE = MOVE_RATE 
+    MOVE_SLEEP = MOVE_SLEEP 
+    XYZ_VELOCITY = XYZ_VELOCITY 
+    ANGLE_VELOCITY_MAX = ANGLE_VELOCITY_MAX 
+
+    GRIPPER_OPEN_MAX = GRIPPER_OPEN_MAX
+    GRIPPER_OPEN_MIN = GRIPPER_OPEN_MIN
+
+    POSITION_UPDATE_INTERVAL = POSITION_UPDATE_INTERVAL 
+    COMMAND_CHECK_INTERVAL = COMMAND_CHECK_INTERVAL 
+
+
+    def log(self, msg):
+        if self.verbose:
+            self.pprint(msg)
+    
+    @staticmethod
+    def pprint(*args, **kwargs):
+        try:
+            stack_tuple = traceback.extract_stack(limit=2)[0]
+            print(
+                "[{}][{}] {}".format(
+                    time.strftime("\033[92m%Y-%m-%d %H:%M:%S\033[0m", time.localtime(time.time())),
+                    stack_tuple[1],
+                    " ".join(map(str, args)),
+                )
+            )
+        except:
+            print(*args, **kwargs)
+
+    def __init__(
+        self,
+        start_time,
+        init_pose=[256.7, 5.1, 400.1, 178.9, 0.0, 1.4],
+        init_servo_angle=[0.0, -45.0, 0.0, 30.0, 0.0, 75.0, 0.0],
+        ip="192.168.1.196", 
+        mode="2D",
+        robot_id=0,
+        command_mode="cartesian",
+        gripper_enable=False,
+        speed=50,  # mm/s
+        verbose=False,
+    ):
+        
+        self.robot_id = robot_id
+        self.init_pose = init_pose
+        self.init_servo_angle = init_servo_angle
+        self.speed = speed
+        self.command_mode = command_mode
+        assert mode in ["2D","3D"], "mode must be 2D or 3D"
+        super().__init__()
+        
+        self.start_time = start_time
+        self._ip = ip
+        self.gripper_enable = gripper_enable
+        self.verbose = verbose
+        self.exe_lock = mp.Lock()
+
+        self.state = mp.Value('i', ControllerState.INIT.value)
+
+        self.cur_trans_q = mp.Queue(maxsize=1)
+        self.cur_qpos_q = mp.Queue(maxsize=1)
+        self.cur_gripper_q = mp.Queue(maxsize=1)
+
+        self.command_receiver = None
+
+        self.cur_gripper_pos = None
+        self.cur_qpos = None
+
+        self.teleop_activated = False
+
+    # ======= xarm controller queue START =======
+    def update_cur_position(self):
+        """ update the current position of the arm in a separate thread,
+        due to the unprecise of get_position API, use sapien fk to do the position closed loop"""
+
+        if self.robot_id == -1:
+            self.state_sender = udpSender(port=XARM_STATE_PORT)
+        try:
+            while self.state.value in [ControllerState.INIT.value, ControllerState.RUNNING.value]:
+
+                if self.gripper_enable:
+                    cur_gripper_pos = self.get_gripper_state()
+                    if not self.cur_gripper_q.full():
+                        self.cur_gripper_q.put(cur_gripper_pos)
+                    self.cur_gripper_pos = cur_gripper_pos
+
+                cur_qpos = np.array(self._arm.get_servo_angle()[1][0:7]) / 180. * np.pi
+
+                fk_trans_mat = self.kin_helper.compute_fk_sapien_links(cur_qpos, [self.kin_helper.sapien_eef_idx])[0]
+                cur_xyzrpy = np.zeros(6)
+                cur_xyzrpy[:3] = fk_trans_mat[:3, 3] * 1000
+                cur_xyzrpy[3:] = transforms3d.euler.mat2euler(fk_trans_mat[:3, :3])
+                cur_xyzrpy[3:] = cur_xyzrpy[3:] / np.pi * 180
+
+                # always update the latest position
+                if not self.cur_trans_q.full():
+                    self.cur_trans_q.put(fk_trans_mat)
+                if not self.cur_qpos_q.full():
+                    self.cur_qpos_q.put(cur_qpos)
+
+                self.cur_qpos = cur_qpos
+
+                if self.robot_id == -1:
+                    state = {
+                        "e2b": fk_trans_mat,
+                        "pos": cur_xyzrpy,
+                        "qpos": cur_qpos,
+                    }
+                    if self.gripper_enable:
+                        state["gripper"] = cur_gripper_pos
+                    self.state_sender.send(state)
+
+        except:
+            self.pprint(f"update_cur_position error")
+            self.state.value = ControllerState.STOP.value
+        finally:
+            # self.state_sender.close()
+            # self.command_q.close()
+            self.cur_trans_q.close()
+            self.cur_qpos_q.close()
+            self.cur_gripper_q.close()
+            print("update_cur_position exit!")
+
+    def get_current_joint(self):
+        return copy.deepcopy(self.cur_qpos)
+    
+    def get_current_gripper(self):
+        return copy.deepcopy(self.cur_gripper_pos)
+
+    def get_current_joint_deg(self):
+        return copy.deepcopy(self.cur_qpos) / np.pi * 180
+
+    def get_current_pose(self):
+        raise NotImplementedError
+        return self.cur_xyzrpy
+
+    # ======= xarm controller queue END =======
+
+
+    # ======= xarm SDK API wrapper START =======
+    def open_gripper(self, wait=True):
+        return self.set_gripper_openness(self.GRIPPER_OPEN_MAX, wait=wait)
+
+    def close_gripper(self, wait=True):
+        return self.set_gripper_openness(self.GRIPPER_OPEN_MIN, wait=wait)
+
+    # @DeprecationWarning
+    def set_gripper_openness(self, openness, wait=True):
+        if not self.is_alive:
+            raise ValueError("Robot is not alive!")
+        code = self._arm.set_gripper_position(openness, wait=wait)
+        if not self._check_code(code, "set_gripper_position"):
+            raise ValueError("close_gripper Error")
+        return True
+
+    def get_gripper_state(self):
+        if not self.is_alive:
+            raise ValueError("Robot is not alive!")
+        code, state = self._arm.get_gripper_position()
+        if not self._check_code(code, "get_gripper_position"):
+            raise ValueError("get_gripper_position Error")
+        return state
+
+    # Register error/warn changed callback
+    def _error_warn_changed_callback(self, data):
+        if data and data["error_code"] != 0:
+            self.alive = False
+            self.pprint("err={}, quit".format(data["error_code"]))
+            self._arm.release_error_warn_changed_callback(
+                self._error_warn_changed_callback
+            )
+
+    # Register state changed callback
+    def _state_changed_callback(self, data):
+        if data and data["state"] == 4:
+            self.alive = False
+            self.pprint("state=4, quit")
+            self._arm.release_state_changed_callback(self._state_changed_callback)
+    
+    # Register count changed callback
+    def _count_changed_callback(self, data):
+        if self.is_alive:
+            self.pprint("counter val: {}".format(data["count"]))
+
+    def _check_code(self, code, label):
+        if not self.is_alive or code != 0:
+            # import ipdb; ipdb.set_trace()
+            self.alive = False
+            ret1 = self._arm.get_state()
+            ret2 = self._arm.get_err_warn_code()
+            self.pprint(
+                "{}, code={}, connected={}, state={}, error={}, ret1={}. ret2={}".format(
+                    label,
+                    code,
+                    self._arm.connected,
+                    self._arm.state,
+                    self._arm.error_code,
+                    ret1,
+                    ret2,
+                )
+            )
+        return self.is_alive
+    # ======= xarm SDK API wrapper END =======
+
+    # ======= xarm control START =======
+    def _init_robot(self):
+        self._arm.clean_warn()
+        self._arm.clean_error()
+        self._arm.motion_enable(True)
+        if self.command_mode == "cartesian":
+            mode = 1
+        elif self.command_mode == "joints":
+            mode = 4
+        else:
+            raise ValueError("Invalid command mode")
+        self._arm.set_mode(mode)  # NOTE: 0: position control mode, 1: servo control mode, 4: velocity control mode
+        self._arm.set_state(0)
+        if self.gripper_enable:
+            self._arm.set_gripper_enable(True)
+            self._arm.set_gripper_mode(0)
+            self._arm.clean_gripper_error()
+        # self._arm.set_collision_sensitivity(1)
+        time.sleep(1)
+        self._arm.register_error_warn_changed_callback(
+            self._error_warn_changed_callback
+        )
+        self._arm.register_state_changed_callback(self._state_changed_callback)
+        if hasattr(self._arm, "register_count_changed_callback"):
+            self._arm.register_count_changed_callback(self._count_changed_callback)
+        
+        self.state.value = ControllerState.RUNNING.value
+
+    def reset(self):
+        # return self._reset()  # position control
+        # return self._reset_pose()  # NOTE: servo control, use sapien ik to move
+        return
+    
+    def _reset(self, wait=True):
+        self.move_to_pose(self.init_pose, wait=wait)
+        self._arm.set_servo_angle(angle=self.init_servo_angle, isradian=False, wait=wait)
+        if self.gripper_enable:
+            self.open_gripper(wait=wait)
+
+    def _reset_pose(self):
+        # init pose
+        if not self.exe_lock.acquire(block=True, timeout=1):
+            self.log("xarm reset failed! exe_lock not acquired!")
+            return
+        
+        self.move(self.init_pose, steps=500, clean=True)
+        self.exe_lock.release()
+
+    def check_valid_move(self, next_position, steps):
+        # absolute position
+        if len(next_position) == 6:
+            x, y, z, roll, pitch, yaw = next_position
+        elif self.gripper_enable and len(next_position) == 7:
+            x, y, z, roll, pitch, yaw, gripper_pos = next_position
+            if gripper_pos < self.GRIPPER_OPEN_MIN or gripper_pos > self.GRIPPER_OPEN_MAX:
+                self.log(f"invalid move command {next_position}! gripper out of range!")
+                return False
+        
+        if x ** 2 + y ** 2 > self.XY_MAX ** 2 or x ** 2 + y ** 2 < self.XY_MIN ** 2\
+            or x < self.X_MIN or x > self.X_MAX or y < self.Y_MIN or y > self.Y_MAX:
+            self.log(f"invalid move command {next_position}! x,y out of range!")
+            return False
+        elif z > self.Z_MAX or z < self.Z_MIN:
+                self.log(f"invalid move command {next_position}! z out of range!")
+                return False
+
+        return True
+
+    def move_joints(self, next_joints, wait=False, ignore_error=False):
+        assert wait == False, "wait is not supported in move_joints"
+        if not self.is_alive:
+            raise ValueError("Robot is not alive!")
+        if self.gripper_enable and len(next_joints) == 8:
+            if not np.isclose(self.cur_gripper_pos, next_joints[-1]):
+                self.set_gripper_openness(next_joints[-1], wait=wait)
+            next_joints = next_joints[:-1]
+        
+        # velocity control (next_joints needs to be delta)
+        v = next_joints / self.COMMAND_CHECK_INTERVAL * 0.15
+        v = v.tolist()
+        code = self._arm.vc_set_joint_velocity(v, is_radian=True, is_sync=False, duration=0)
+        
+        # position control
+        # next_joints = next_joints.tolist()
+        # code = self._arm.set_servo_angle_j(angles=next_joints, is_radian=True, speed=1.0, acc=None, wait=wait)
+        
+        if not self._check_code(code, "vc_set_joint_velocity"):
+            raise ValueError("move_joints Error")
+        if ignore_error:
+            self._arm.clean_error()
+            self._arm.clean_warn()
+
+    def move_to_pose(self, pose, wait=False, ignore_error=False):
+        return self.move(pose)
+
+    def move(self, next_position, steps=10, clean=True):
+        self._move_ik(next_position, steps, clean)  # NOTE: use sapien ik to move
+        # self._move(next_position, steps, clean)
+
+    def _move(self, pose, steps, clean):
+        if not self.is_alive:
+            raise ValueError("Robot is not alive!")
+        code = self._arm.set_position(
+            pose[0], pose[1], pose[2], pose[3], pose[4], pose[5], speed=self.speed, wait=True
+        )
+        if not self._check_code(code, "set_position"):
+            raise ValueError("move_to_pose Error")
+        if clean:
+            self._arm.clean_error()
+            self._arm.clean_warn()
+
+    def _move_ik(self, next_position, steps, clean):
+        """next_position :  x,y,z in mm   and   r,p,y in degree  [and gripper]"""
+        assert next_position is not None, "next_position is not set!"
+        next_position = np.array(next_position)
+        if not self.check_valid_move(next_position, steps):
+            print(f"invalid move command {next_position}!")
+            return
+        
+        self.log(f'move start: {next_position}')
+
+        if self.gripper_enable and len(next_position) == 8:
+            if not np.isclose(self.cur_gripper_pos, next_position[-1]):
+                self.set_gripper_openness(next_position[-1])
+            next_position = next_position[:-1]
+
+        initial_qpos = np.array(self._arm.get_servo_angle()[1][0:7]) / 180. * np.pi
+        next_position_m_rad = np.zeros_like(np.array(next_position))
+        next_position_m_rad[0:3] = np.array(next_position)[0:3] / 1000.
+        next_position_m_rad[3:] = np.array(next_position)[3:] / 180. * np.pi
+        next_servo_angle = self.kin_helper.compute_ik_sapien(initial_qpos, next_position_m_rad)
+
+        # fix the eef joint to [-pi,pi]
+        next_servo_angle[-1] = (next_servo_angle[-1] + np.pi) % (2 * np.pi) - np.pi
+
+        # NOTE: In the servo mode: state(1), the actual speed is contorlled by the
+        #       rate of command sending and the the distance between the current and target position 
+        # The steps of each move is decided by the distance of moving to keep speed constant
+        init_position = self.kin_helper.compute_fk_sapien_links(initial_qpos, [self.kin_helper.sapien_eef_idx])[0][:3,3] * 1000
+        dis_diff = np.array(next_position[:3]) - np.array(init_position)
+        distance = np.linalg.norm(dis_diff) # in millimeter
+        min_steps = int(distance / self.XYZ_VELOCITY)
+        self.log(f"distance: {distance}, min_steps: {min_steps}")
+        steps = max(min_steps, steps)
+
+        # TODO: add max angular velocity control
+        angle_diff = np.array(next_servo_angle) - np.array(initial_qpos)
+        angle_distance = np.max(abs(angle_diff)) *180 / np.pi
+        self.log(f"angle_distance: {angle_distance}")
+        self.log(f"last steps: {steps}, angle ref steps: {int(angle_distance / self.ANGLE_VELOCITY_MAX)}")
+        steps = max(steps, int(angle_distance / self.ANGLE_VELOCITY_MAX))
+
+        tic = time.time()
+        for i in range(steps): 
+            angle = initial_qpos + (next_servo_angle - initial_qpos) * (i + 1) / steps
+            if not self.is_alive:
+                raise ValueError("Robot is not alive!")
+            code = self._arm.set_servo_angle_j(angles=angle, is_radian=True, speed=1)
+            if not self._check_code(code, "set_position"):
+                raise ValueError("move Error")
+            time.sleep(self.MOVE_SLEEP)
+
+        self.log(f"move end: volecity: {distance/(time.time()-tic):.2f} mm/s")
+
+        if clean:
+            self._arm.clean_error()
+            self._arm.clean_warn()
+
+    # ======= xarm control END =======
+
+    # ======= main thread loop =======
+    def run(self):
+        # the arm initialization must be invoked in the same process
+        print("Connecting to xarm at", self._ip)
+        self._arm = XArmAPI(self._ip)
+        print("Connected to xarm at", self._ip)
+        if self.verbose:
+            self.log(f"Connected to xarm at {self._ip}")
+        self._init_robot()
+
+        # NOTE: the arm can only be interacted with the process created the API class, 
+        self.update_pos_t = threading.Thread(target=self.update_cur_position, name="update_cur_position")
+        self.update_pos_t.start()
+
+        port = XARM_CONTROL_PORT_L if self.robot_id <= 0 else XARM_CONTROL_PORT_R  # -1: keyboard, 0: gello left, 1: gello right
+        self.command_receiver = udpReceiver(ports={'xarm_control': port})
+        self.command_receiver.start()
+
+        print(f"{'='*20} xarm start! state: {ControllerState(self.state.value)}")
+        self.reset()
+        print(f"{'='*20} xarm reset! state: {ControllerState(self.state.value)}")
+        
+        command = None
+        self.teleop_activated = False if self.command_mode == 'joints' else True
+        rate = Rate(
+            duration=COMMAND_CHECK_INTERVAL,
+        )
+
+        while self.state.value == ControllerState.RUNNING.value:
+            try:
+                start_time = time.time()
+                commands = self.command_receiver.get("xarm_control", pop=True)
+                if commands is None or len(commands) == 0:
+                    if self.command_mode == 'joints':  # velocity control
+                        self._arm.vc_set_joint_velocity([0, 0, 0, 0, 0, 0, 0], is_radian=True, is_sync=False, duration=0)
+                    rate.sleep()
+                    continue
+
+                print_commands = False
+                if len(commands[0]) > 0 and print_commands:
+                    if self.robot_id == 1:
+                        print('\t' * 12, end='')
+                    print(f'activated: {self.teleop_activated}, commands: {[np.round(c, 4) for c in commands[0]]}')
+
+                with self.exe_lock:
+                    self.log("xarm controller get lock")
+                    self.log(f"new commands at {time.time() - self.start_time:.2f}")
+                    
+                    for command in commands:
+                        self.log(f"get command: {command}")
+
+                        if isinstance(command, str):
+                            if command == "quit":
+                                break
+
+                        if self.command_mode == 'cartesian':
+                            self.move(command)
+
+                        if self.command_mode == 'joints':
+                            command_state = np.array(command)
+
+                            assert len(command_state) == 8, "command state must be 8-dim"
+
+                            current_joints = self.get_current_joint()
+                            current_gripper = self.get_current_gripper()
+                            current_gripper = (current_gripper - GRIPPER_OPEN_MAX) / (GRIPPER_OPEN_MIN - GRIPPER_OPEN_MAX)
+                            current_state = np.concatenate([current_joints, np.array([current_gripper])])
+
+                            delta = command_state - current_state
+                            joint_delta_norm = np.linalg.norm(delta[0:7])
+                            max_joint_delta = np.abs(delta[0:7]).max()
+
+                            max_activate_delta = 0.5
+                            max_delta_norm = 0.10
+                            if not self.teleop_activated:
+                                if max_joint_delta < max_activate_delta:
+                                    self.teleop_activated = True
+                                next_state = current_state
+                            else:
+                                if joint_delta_norm > max_delta_norm:
+                                    delta[0:7] = delta[0:7] / joint_delta_norm * max_delta_norm
+                                next_state = current_state + delta
+                            
+                            next_state[0:7] = next_state[0:7] - current_state[0:7]
+                            
+                            # denormalize gripper position
+                            gripper_pos = next_state[-1]
+                            denormalized_gripper_pos = gripper_pos * (GRIPPER_OPEN_MIN - GRIPPER_OPEN_MAX) + GRIPPER_OPEN_MAX
+                            next_state[-1] = denormalized_gripper_pos
+
+                            self.move_joints(next_state)
+
+                    if command == "quit":
+                        break
+                
+                rate.sleep()
+            
+            except:
+                self.log(f"Error in xarm controller")
+                break
+
+        self.stop()
+        self.command_receiver.stop()
+        print("xarm controller stopped")
+
+    # ======= process control =======
+    # Only the process created the API class can start and control the robot, init in the `run` function 
+    
+    def start(self) -> None:
+        return super().start()
+    
+    def stop(self):
+        self.state.value = ControllerState.STOP.value
+        print(f"{'='*20} xarm exit! state: {ControllerState(self.state.value)}")
+
+    @property
+    def is_alive(self):
+        """check whether the robot and the controller is alive
+        To check only the controller condition, use self.state.value == ControllerState.RUNNING.value"""
+        if self.is_controller_alive and self._arm.connected and self._arm.error_code == 0:
+            if self._arm.state == 5:
+                cnt = 0
+                while self._arm.state == 5 and cnt < 5:
+                    cnt += 1
+                    time.sleep(0.1)
+            return self._arm.state < 4
+        else:
+            return False
+
+    @property
+    def is_controller_alive(self):
+        return self.state.value == ControllerState.RUNNING.value
+
+    
+if __name__ == "__main__":
+
+    start_time = time.time()
+    controller = XarmController(
+        start_time=start_time,
+        gripper_enable=False,
+        z_plane_height=434,
+        verbose=True,)
+    controller.start()
+    controller.join()
diff --git a/src/experiments/real_world/plan.py b/src/experiments/real_world/plan.py
new file mode 100644
index 0000000000000000000000000000000000000000..0f6a85b9b9065e6b36cc7adf19f6f890966e160a
--- /dev/null
+++ b/src/experiments/real_world/plan.py
@@ -0,0 +1,89 @@
+from pathlib import Path
+import argparse
+import random
+import time
+import os
+import matplotlib.pyplot as plt
+from collections import defaultdict
+from tqdm import tqdm, trange
+import hydra
+from omegaconf import DictConfig, OmegaConf
+import yaml
+from datetime import datetime
+import numpy as np
+from PIL import Image
+import warp as wp
+import matplotlib.pyplot as plt
+import multiprocess as mp
+import torch
+import torch.backends.cudnn
+import torch.nn as nn
+from torch.nn.utils import clip_grad_norm_
+from torch.utils.data import DataLoader
+import logging
+
+from pgnd.utils import get_root, mkdir
+from modules_planning.planning_env import RobotPlanningEnv
+
+root: Path = get_root(__file__)
+logging.basicConfig(level=logging.WARNING)
+
+
+def main(args):
+    mp.set_start_method('spawn')
+
+    with open(root / args.config, 'r') as f:
+        config = yaml.load(f, Loader=yaml.CLoader)
+    cfg = OmegaConf.create(config)
+
+    cfg.sim.num_steps = 1000
+    cfg.sim.gripper_forcing = False
+    cfg.sim.uniform = True
+
+    iteration = args.iteration
+    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+    ckpt_path = (root / args.config).parent / 'ckpt' / f'{iteration:06d}.pt'
+    seed = cfg.seed
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+
+    # path
+    datetime_now = datetime.now().strftime('%y%m%d-%H%M%S')
+    exp_root: Path = root / 'log' / cfg.train.name / 'plan' / datetime_now
+    mkdir(exp_root, overwrite=cfg.overwrite, resume=cfg.resume)
+
+    env = RobotPlanningEnv(
+        cfg,
+        exp_root=exp_root,
+        ckpt_path=ckpt_path,
+        resolution=(848, 480),
+        capture_fps=30,
+        record_fps=0,
+        text_prompts=args.text_prompts,
+        show_annotation=(not args.no_annotation),
+        use_robot=True,
+        bimanual=args.bimanual,
+        gripper_enable=True,
+        debug=True,
+        construct_target=args.construct_target,
+    )
+
+    env.start()
+    env.join()
+
+
+if __name__ == '__main__':
+    arg_parser = argparse.ArgumentParser()
+    arg_parser.add_argument('--config', type=str, default='log/cloth/train/hydra.yaml')
+    arg_parser.add_argument('--iteration', type=str, default=100000)
+    arg_parser.add_argument('--text_prompts', type=str, default='green towel.')
+    arg_parser.add_argument('--seed', type=int, default=42)
+    arg_parser.add_argument('--no_annotation', action='store_true')
+    arg_parser.add_argument('--bimanual', action='store_true')
+    arg_parser.add_argument('--construct_target', action='store_true')
+    args = arg_parser.parse_args()
+
+    with torch.no_grad():
+        main(args)
diff --git a/src/experiments/real_world/reconstruct_gs.py b/src/experiments/real_world/reconstruct_gs.py
new file mode 100644
index 0000000000000000000000000000000000000000..79e2259331489602289ea5ec62abc34b44d61b2e
--- /dev/null
+++ b/src/experiments/real_world/reconstruct_gs.py
@@ -0,0 +1,283 @@
+from typing import Union
+from pathlib import Path
+import argparse
+import os
+import subprocess
+import numpy as np
+import glob
+import cv2
+import torch
+import shutil
+from PIL import Image
+from sklearn.neighbors import NearestNeighbors
+import supervision as sv
+import open3d as o3d
+import time
+import yaml
+import json
+from dgl.geometry import farthest_point_sampler
+import sys
+sys.path.append(str(Path(__file__).parent.parent))
+sys.path.append(str(Path(__file__).parent.parent.parent))
+
+from pgnd.utils import get_root
+from pgnd.ffmpeg import make_video
+root: Path = get_root(__file__)
+
+from utils.pcd_utils import visualize_o3d, depth2fgpcd
+from utils.env_utils import get_bounding_box
+from gs.trainer import GSTrainer
+from gs.convert import save_to_splat
+
+
+def load_camera(episode_data_dir):
+    intr = np.load(episode_data_dir / 'calibration' / 'intrinsics.npy').astype(np.float32)
+    rvec = np.load(episode_data_dir / 'calibration' / 'rvecs.npy')
+    tvec = np.load(episode_data_dir / 'calibration' / 'tvecs.npy')
+    R = [cv2.Rodrigues(rvec[i])[0] for i in range(rvec.shape[0])]
+    T = [tvec[i, :, 0] for i in range(tvec.shape[0])]
+    extrs = np.zeros((len(R), 4, 4)).astype(np.float32)
+    for i in range(len(R)):
+        extrs[i, :3, :3] = R[i]
+        extrs[i, :3, 3] = T[i]
+        extrs[i, 3, 3] = 1
+    return intr, extrs
+
+
+def project(points, intr, extr):
+    # extr: (4, 4)
+    # intr: (3, 3)
+    # points: (n_points, 3)
+    points = np.concatenate([points, np.ones((points.shape[0], 1))], axis=1)
+    points = points @ extr.T  # (n_points, 4)
+    points = points[:, :3] / points[:, 2:3]  # (n_points, 3)
+    points = points @ intr.T
+    points = points[:, :2] / points[:, 2:3]  # (n_points, 2)
+    return points
+
+
+def reproject(depth, intr, extr):
+    xx, yy = np.meshgrid(np.arange(depth.shape[1]), np.arange(depth.shape[0]))
+    xx = xx.flatten()
+    yy = yy.flatten()
+    points = np.stack([xx, yy, depth.flatten()], axis=1)
+    
+    mask = depth.flatten() > 0
+    mask = np.logical_and(mask, depth.flatten() < 2)
+    points = points[mask]
+
+    fx = intr[0, 0]
+    fy = intr[1, 1]
+    cx = intr[0, 2]
+    cy = intr[1, 2]
+    points[:, 0] = (points[:, 0] - cx) / fx * points[:, 2]
+    points[:, 1] = (points[:, 1] - cy) / fy * points[:, 2]
+    points = np.concatenate([points, np.ones((points.shape[0], 1))], axis=1)
+    inv_extr = np.linalg.inv(extr)
+    points = points @ inv_extr.T
+    return points[:, :3]
+
+
+class GSProcessor:
+
+    def __init__(self, name, n_his_frames, device='cuda', episode_range=None):
+        self.name = name
+        self.n_his_frames = n_his_frames
+        self.data_dir = root / "log" / "data" / name
+        
+        self.device = device
+
+        if 'merged' in name:
+            self.is_merged = True
+            assert os.path.exists(self.data_dir)
+            info = json.load(open(self.data_dir / 'sub_episodes_v' / 'info.json'))
+
+            source_eval_list_all = []
+            for source_dir, episode_list in info.items():
+                if source_dir in ['n_train', 'n_eval']:
+                    continue
+
+                source_name = Path(source_dir).parent.name
+
+                eval_episode_start, eval_episode_end = episode_list[2], episode_list[3]
+
+                source_eval_list = []  # (episode_id, start_frame, end_frame)
+                
+                source_data_dir = root.parent / Path(source_dir).parent
+                if not os.path.exists(root.parent / source_dir):
+                    source_data_dir = Path('/data/meta-material/data') / source_name
+
+                for eval_episode in range(eval_episode_start, eval_episode_end):
+                    assert os.path.exists(source_data_dir / 'sub_episodes_v' / f'episode_{eval_episode:04d}' / 'meta.txt')
+                    meta = np.loadtxt(source_data_dir / 'sub_episodes_v' / f'episode_{eval_episode:04d}' / 'meta.txt')
+
+                    source_eval_list.append((source_data_dir, int(meta[0]), int(meta[1]), int(meta[2])))
+
+                source_eval_list_all.append(source_eval_list)
+            
+            episode_range = source_eval_list_all
+
+        else:
+            self.is_merged = False
+            if episode_range is None:
+                n_episodes = len(glob.glob(str(self.data_dir / "episode*")))
+                episode_range = np.arange(n_episodes)
+
+        self.episodes = episode_range
+
+        n_cameras = 4
+        self.cameras = np.arange(n_cameras)
+
+        self.max_frames = 10000
+        self.H, self.W = 480, 848
+        self.bbox = get_bounding_box()  # 3D bounding box of the scene
+
+        with open(root / "real_world" / "gs" / "config" / "default.yaml", 'r') as f:
+            gs_config = yaml.load(f, Loader=yaml.CLoader)
+
+        self.gs_trainer = GSTrainer(gs_config, device=self.device)
+    
+    def get_gaussian(self):
+        if self.is_merged:
+            for source_eval_list in self.episodes:
+                self.get_gaussian_single_source(source_eval_list)
+        else:
+            self.get_gaussian_single_source(self.episodes)
+
+    def get_gaussian_single_source(self, episodes):
+        for episode in episodes:
+
+            if isinstance(episode, tuple):
+                data_dir = episode[0]
+
+                episode_id = episode[1]
+                start_frame = episode[2]
+                end_frame = episode[3]
+
+                start_frame = start_frame + self.n_his_frames
+                
+                episode_data_dir = data_dir / f"episode_{episode_id:04d}"
+                os.makedirs(episode_data_dir / "gs", exist_ok=True)
+                intrs, extrs = load_camera(episode_data_dir)
+
+                pcd_paths = sorted(glob.glob(str(episode_data_dir / "pcd_clean_new" / "*.npz")))
+                n_frames = min(len(pcd_paths), self.max_frames)
+
+                assert start_frame < n_frames, f"episode {episode}"
+                assert end_frame <= n_frames
+                
+            else:
+                episode = int(episode)
+                data_dir = self.data_dir
+                episode_id = episode
+                start_frame = 0
+
+                episode_data_dir = data_dir / f"episode_{episode_id:04d}"
+                os.makedirs(episode_data_dir / "gs", exist_ok=True)
+                intrs, extrs = load_camera(episode_data_dir)
+
+                pcd_paths = sorted(glob.glob(str(episode_data_dir / "pcd_clean_new" / "*.npz")))
+                n_frames = min(len(pcd_paths), self.max_frames)
+
+                end_frame = n_frames
+
+            pivot_skip = 120
+
+            for frame_id in range(start_frame, end_frame, pivot_skip):
+                print(f'[get_gaussian] processing episode {episode_id}, frame {frame_id}')
+                
+                if os.path.exists(os.path.join(episode_data_dir / 'gs' / f'{frame_id:06d}.splat')):
+                    print(f'[get_gaussian] already processed, skipping')
+                    continue
+
+                pcd_npz = np.load(episode_data_dir / "pcd_clean_new" / f"{frame_id:06d}.npz")
+
+                pts = pcd_npz['pts']
+                colors = pcd_npz['colors']
+
+                # downsample
+                n_points = 3000
+                particle_tensor = torch.from_numpy(pts).float().cuda()[None]
+                fps_idx_tensor = farthest_point_sampler(particle_tensor, n_points, start_idx=0)[0]
+                pts = pts[fps_idx_tensor.cpu().numpy()]
+                colors = colors[fps_idx_tensor.cpu().numpy()]
+                if colors.max() > 1:
+                    colors = colors.astype(np.float32) / 255
+
+                pcd = o3d.geometry.PointCloud()
+                pcd.points = o3d.utility.Vector3dVector(pts)
+                pcd.colors = o3d.utility.Vector3dVector(colors)
+
+                imgs = []
+                masks = []
+                R_list = []
+                t_list = []
+                intr_list = []
+                for camera in self.cameras:
+                    rgb_path = str(episode_data_dir / f'camera_{camera}' / 'rgb' / f'{frame_id:06d}.jpg')
+                    mask_path = str(episode_data_dir / f'camera_{camera}' / 'mask' / f'{frame_id:06d}.png')
+                    img = cv2.imread(rgb_path)  # bgr
+                    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+                    mask = cv2.imread(mask_path, cv2.IMREAD_UNCHANGED)
+                    mask = mask > 0  # binary mask
+                    imgs.append(img)
+                    masks.append(mask)
+                    w2c = extrs[camera]
+                    c2w = np.linalg.inv(w2c)
+                    R = c2w[:3, :3]
+                    t = c2w[:3, 3]
+                    R_list.append(R)
+                    t_list.append(t)
+                    intr_list.append(intrs[camera])
+
+                # save
+                debug_vis = False
+                if debug_vis:
+                    for i in range(len(imgs)):
+                        # project points
+                        points = project(pts, intrs[i], extrs[i])
+                        points = points.astype(np.int32)
+                        img = imgs[i].copy()
+                        img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
+                        for j in range(points.shape[0]):
+                            if points[j, 0] < 0 or points[j, 0] >= self.W or points[j, 1] < 0 or points[j, 1] >= self.H:
+                                continue
+                            if not masks[i][points[j, 1], points[j, 0]]:
+                                continue
+                            cv2.circle(img, (points[j, 0], points[j, 1]), 2, (255, 0, 0), -1)
+                        
+                        cv2.imwrite(f"{frame_id:06d}_{i}_proj.png", img)
+                        cv2.imwrite(f"{frame_id:06d}_{i}_rgb.png", cv2.cvtColor(imgs[i], cv2.COLOR_RGB2BGR))
+                        cv2.imwrite(f"{frame_id:06d}_{i}_mask.png", (masks[i] * 1).astype(np.uint8))
+
+                self.gs_trainer.clear(clear_params=True)
+                self.gs_trainer.update_state_no_env(pcd, imgs, masks, R_list, t_list, intr_list, n_cameras=4)
+            
+                os.makedirs(root / "log/gs/train", exist_ok=True)
+                self.gs_trainer.train(vis_dir=str(root / "log/gs/train"))
+
+                self.gs_dir = os.path.join(episode_data_dir / "gs" / f'{frame_id:06d}.splat')
+                save_to_splat(
+                    pts=self.gs_trainer.params['means3D'].detach().cpu().numpy(),
+                    colors=self.gs_trainer.params['rgb_colors'].detach().cpu().numpy(),
+                    scales=torch.exp(self.gs_trainer.params['log_scales']).detach().cpu().numpy(),
+                    quats=torch.nn.functional.normalize(self.gs_trainer.params['unnorm_rotations'], dim=-1).detach().cpu().numpy(),
+                    opacities=torch.sigmoid(self.gs_trainer.params['logit_opacities']).detach().cpu().numpy(),
+                    output_file=self.gs_dir,
+                    center=False,  # do not center the points
+                    rotate=False,  # do not rotate the points to swap z and y
+                )
+
+
+if __name__ == '__main__':
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--name', type=str, default='rope_merged')
+    parser.add_argument('--n_his_frames', type=int, default=6)
+    parser.add_argument('--device', type=int, default=0)
+    args = parser.parse_args()
+
+    device = f"cuda:{args.device}"
+
+    pp = GSProcessor(args.name, args.n_his_frames, device)
+    pp.get_gaussian()
diff --git a/src/experiments/real_world/utils/__init__.py b/src/experiments/real_world/utils/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/src/experiments/real_world/utils/env_utils.py b/src/experiments/real_world/utils/env_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..5cdcde83a2630cd932641a4f7a398e036da68ba4
--- /dev/null
+++ b/src/experiments/real_world/utils/env_utils.py
@@ -0,0 +1,9 @@
+import numpy as np
+
+
+def get_bounding_box_bimanual():
+    return np.array([[0.0, 0.6], [-0.35, 0.45 + 0.75], [-0.65, 0.05]])  # the world frame robot workspace
+
+
+def get_bounding_box():
+    return np.array([[0.0, 0.6], [-0.35, 0.45], [-0.65, 0.05]])  # the world frame robot workspace
diff --git a/src/experiments/real_world/utils/gradio_utils.py b/src/experiments/real_world/utils/gradio_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..b9411350b468ebeff4bf8de962de324b640d90d3
--- /dev/null
+++ b/src/experiments/real_world/utils/gradio_utils.py
@@ -0,0 +1,218 @@
+import gradio as gr
+import numpy as np
+from PIL import Image, ImageDraw
+
+
+
+def get_valid_mask(mask: np.ndarray):
+    """Convert mask from gr.Image(0 to 255, RGBA) to binary mask.
+    """
+    if mask.ndim == 3:
+        mask_pil = Image.fromarray(mask).convert('L')
+        mask = np.array(mask_pil)
+    if mask.max() == 255:
+        mask = mask / 255
+    return mask
+
+
+def draw_points_on_image(image, points, intr=None, extr=None, z=None, radius_scale=0.006, intr_orig=None, extr_orig=None):
+    use_2d = (intr is None and extr is None)
+    same_cam = (intr_orig is None and extr_orig is None)
+    if use_2d:
+        assert same_cam
+    else:
+        assert intr is not None
+        assert extr is not None
+        assert z is not None
+    if intr_orig is None:
+        assert extr_orig is None
+        intr_orig = intr
+        extr_orig = extr
+    overlay_rgba = Image.new("RGBA", image.size, 0)
+    overlay_draw = ImageDraw.Draw(overlay_rgba)
+    for point_key, point in points.items():
+
+        t_color = (255, 100, 100)
+        o_color = (255, 50, 50)
+
+        rad_draw = int(image.size[0] * radius_scale) + 2
+
+        p_start = point["start"]
+        p_target = point["target"]
+
+        if p_start is not None and p_target is not None:
+            if same_cam:
+                p_draw = int(p_start[0]), int(p_start[1])
+                t_draw = int(p_target[0]), int(p_target[1])
+
+            # 2d
+            if use_2d:
+                pt = (p_target[0] - p_start[0], p_target[1] - p_start[1])
+                pt_norm = np.linalg.norm(pt)
+                pt_unit = (pt[0] / pt_norm, pt[1] / pt_norm)
+                pt_tang = (pt_unit[1], -pt_unit[0])
+                tt1 = (t_draw[0] + pt_tang[0] * 0.1 * pt_norm - pt_unit[0] * 0.1 * pt_norm,
+                    t_draw[1] + pt_tang[1] * 0.1 * pt_norm - pt_unit[1] * 0.1 * pt_norm)
+                tt2 = (t_draw[0] - pt_tang[0] * 0.1 * pt_norm - pt_unit[0] * 0.1 * pt_norm,
+                    t_draw[1] - pt_tang[1] * 0.1 * pt_norm - pt_unit[1] * 0.1 * pt_norm)
+
+            # 3d
+            else:
+                p_start_3d = np.array([p_start[0], p_start[1], 1])
+                p_target_3d = np.array([p_target[0], p_target[1], 1])
+                p_start_3d = np.dot(np.linalg.inv(intr_orig), p_start_3d)
+                p_target_3d = np.dot(np.linalg.inv(intr_orig), p_target_3d)
+                p_start_3d = np.dot(np.linalg.inv(extr_orig), np.concatenate([p_start_3d, [1]]))
+                p_target_3d = np.dot(np.linalg.inv(extr_orig), np.concatenate([p_target_3d, [1]]))
+                camera_t = np.linalg.inv(extr_orig)[:3, 3]
+                p_start_3d = (p_start_3d[:3] - camera_t) * (z - camera_t[2]) / (p_start_3d[2] - camera_t[2]) + camera_t
+                p_target_3d = (p_target_3d[:3] - camera_t) * (z - camera_t[2]) / (p_target_3d[2] - camera_t[2]) + camera_t
+                pt_3d = p_target_3d - p_start_3d
+                pt_3d_norm = np.linalg.norm(pt_3d)
+                pt_3d_unit = pt_3d / pt_3d_norm
+                pt_3d_tang = np.array([pt_3d_unit[1], -pt_3d_unit[0], 0])
+                tt1_3d = p_target_3d + pt_3d_tang * 0.02 - pt_3d_unit * 0.02
+                tt2_3d = p_target_3d - pt_3d_tang * 0.02 - pt_3d_unit * 0.02
+                tt1_3d = np.dot(extr, np.concatenate([tt1_3d, [1]]))[:3]
+                tt2_3d = np.dot(extr, np.concatenate([tt2_3d, [1]]))[:3]
+                tt1_3d = np.dot(intr, tt1_3d)
+                tt2_3d = np.dot(intr, tt2_3d)
+                tt1_3d = (tt1_3d[:2] / tt1_3d[2]).astype(int)
+                tt2_3d = (tt2_3d[:2] / tt2_3d[2]).astype(int)
+                tt1 = (tt1_3d[0], tt1_3d[1])
+                tt2 = (tt2_3d[0], tt2_3d[1])
+
+            tt1_draw = int(tt1[0]), int(tt1[1])
+            tt2_draw = int(tt2[0]), int(tt2[1])
+
+            if not same_cam:
+                assert not use_2d
+                p_proj = np.dot(intr, np.dot(extr, np.concatenate([p_start_3d, [1]]))[:3])
+                p_draw = (p_proj[:2] / p_proj[2]).astype(int)
+                t_proj = np.dot(intr, np.dot(extr, np.concatenate([p_target_3d, [1]]))[:3])
+                t_draw = (t_proj[:2] / t_proj[2]).astype(int)
+
+            overlay_draw.line(
+                (p_draw[0], p_draw[1], t_draw[0], t_draw[1]),
+                fill=o_color,
+                width=4,
+            )
+            
+            overlay_draw.line(
+                (t_draw[0], t_draw[1], tt1_draw[0], tt1_draw[1]),
+                fill=o_color,
+                width=4,
+            )
+
+            overlay_draw.line(
+                (t_draw[0], t_draw[1], tt2_draw[0], tt2_draw[1]),
+                fill=o_color,
+                width=4,
+            )
+
+        if p_start is not None:
+            if same_cam:
+                p_draw = int(p_start[0]), int(p_start[1])
+            else:
+                assert not use_2d
+                # 3d
+                p_start_3d = np.array([p_start[0], p_start[1], 1])
+                p_start_3d = np.dot(np.linalg.inv(intr_orig), p_start_3d)
+                p_start_3d = np.dot(np.linalg.inv(extr_orig), np.concatenate([p_start_3d, [1]]))
+                camera_t = np.linalg.inv(extr_orig)[:3, 3]
+                p_start_3d = (p_start_3d[:3] - camera_t) * (z - camera_t[2]) / (p_start_3d[2] - camera_t[2]) + camera_t
+                
+                p_proj = np.dot(intr, np.dot(extr, np.concatenate([p_start_3d, [1]]))[:3])
+                p_draw = (p_proj[:2] / p_proj[2]).astype(int)
+            
+            overlay_draw.ellipse(
+                (
+                    p_draw[0] - rad_draw,
+                    p_draw[1] - rad_draw,
+                    p_draw[0] + rad_draw,
+                    p_draw[1] + rad_draw,
+                ),
+                fill=t_color,
+                outline=o_color,
+                width=2,
+            )
+
+        if p_target is not None:
+            assert p_start is not None
+
+    return Image.alpha_composite(image.convert("RGBA"),
+                                 overlay_rgba).convert("RGB")
+
+
+def draw_raw_points_on_image(image,
+                             points,
+                             radius_scale=0.002):
+    overlay_rgba = Image.new("RGBA", image.size, 0)
+    overlay_draw = ImageDraw.Draw(overlay_rgba)
+    for p in range(points.shape[0]):
+        point = points[p]
+
+        t_color = (150, 150, 255)
+        o_color = (50, 50, 255)
+
+        rad_draw = int(image.size[0] * radius_scale)
+
+        t_draw = int(point[0]), int(point[1])
+        overlay_draw.ellipse(
+            (
+                t_draw[0] - rad_draw,
+                t_draw[1] - rad_draw,
+                t_draw[0] + rad_draw,
+                t_draw[1] + rad_draw,
+            ),
+            fill=t_color,
+            outline=o_color,
+        )
+
+    return Image.alpha_composite(image.convert("RGBA"),
+                                 overlay_rgba).convert("RGB")
+
+
+def draw_mask_on_image(image, mask):
+    im_mask = np.uint8(mask * 255)
+    im_mask_rgba = np.concatenate(
+        (
+            np.tile(im_mask[..., None], [1, 1, 3]),
+            45 * np.ones(
+                (im_mask.shape[0], im_mask.shape[1], 1), dtype=np.uint8),
+        ),
+        axis=-1,
+    )
+    im_mask_rgba = Image.fromarray(im_mask_rgba).convert("RGBA")
+
+    return Image.alpha_composite(image.convert("RGBA"),
+                                 im_mask_rgba).convert("RGB")
+
+
+def on_change_single_global_state(keys,
+                                  value,
+                                  global_state,
+                                  map_transform=None):
+    if map_transform is not None:
+        value = map_transform(value)
+
+    curr_state = global_state
+    if isinstance(keys, str):
+        last_key = keys
+
+    else:
+        for k in keys[:-1]:
+            curr_state = curr_state[k]
+
+        last_key = keys[-1]
+
+    curr_state[last_key] = value
+    return global_state
+
+
+def get_latest_points_pair(points_dict):
+    if not points_dict:
+        return None
+    point_idx = list(points_dict.keys())
+    latest_point_idx = max(point_idx)
+    return latest_point_idx
\ No newline at end of file
diff --git a/src/experiments/real_world/utils/pcd_utils.py b/src/experiments/real_world/utils/pcd_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..fd15da3a4b35651bea1728df506c8cadce9cd8c0
--- /dev/null
+++ b/src/experiments/real_world/utils/pcd_utils.py
@@ -0,0 +1,166 @@
+import numpy as np
+import open3d as o3d
+
+
+def filter_tabletop_points(pcd):
+    # RANSAC to find table plane
+    plane_model, inliers = pcd.segment_plane(distance_threshold=0.01, ransac_n=3, num_iterations=1000)
+    [a, b, c, d] = plane_model
+    table_plane = np.array([a, b, c, d])
+
+    # remove points on the table
+    pcd = pcd.select_by_index(inliers, invert=True)
+
+    outliers = None
+    new_outlier = None
+    rm_iter = 0
+    while new_outlier is None or len(new_outlier.points) > 0:
+        _, inlier_idx = pcd.remove_statistical_outlier(
+            nb_neighbors = 20, std_ratio = 1.5 + rm_iter * 0.5
+        )
+        new_pcd = pcd.select_by_index(inlier_idx)
+        new_outlier = pcd.select_by_index(inlier_idx, invert=True)
+        if outliers is None:
+            outliers = new_outlier
+        else:
+            outliers += new_outlier
+        pcd = new_pcd
+        rm_iter += 1
+
+    return pcd
+
+
+def get_tabletop_points(rgb_list, depth_list, R_list, t_list, intr_list, bbox, depth_threshold=[0, 2]):
+    # increase if out of memory
+    stride = 1
+
+    pcd_all = o3d.geometry.PointCloud()
+
+    for i in range(len(rgb_list)):
+        intr = intr_list[i]
+        R_cam2board = R_list[i]
+        t_cam2board = t_list[i]
+
+        depth = depth_list[i].copy().astype(np.float32)
+
+        points = depth2fgpcd(depth, intr)
+        points = points.reshape(depth.shape[0], depth.shape[1], 3)
+        points = points[::stride, ::stride, :].reshape(-1, 3)
+
+        mask = np.logical_and(
+            (depth > depth_threshold[0]), (depth < depth_threshold[1])
+        )  # (H, W)
+        mask = mask[::stride, ::stride].reshape(-1)
+
+        img = rgb_list[i].copy()
+
+        points = points[mask].reshape(-1, 3)
+
+        points = R_cam2board @ points.T + t_cam2board[:, None]
+        points = points.T  # (N, 3)
+
+        pcd = o3d.geometry.PointCloud()
+        pcd.points = o3d.utility.Vector3dVector(points)
+
+        colors = img[::stride, ::stride, :].reshape(-1, 3).astype(np.float64)
+        colors = colors[mask].reshape(-1, 3)
+        colors = colors[:, ::-1].copy()
+        pcd.colors = o3d.utility.Vector3dVector(colors / 255)
+        pcd_all += pcd
+
+    pcd = pcd_all
+    pcd = pcd.crop(o3d.geometry.AxisAlignedBoundingBox(min_bound=bbox[:, 0], max_bound=bbox[:, 1]))
+    pcd = pcd.voxel_down_sample(voxel_size=0.001)
+
+    # NOTE: simple filtering instead of instance-specific segmentation processing
+    pcd = filter_tabletop_points(pcd)
+    return pcd
+
+
+def rpy_to_rotation_matrix(roll, pitch, yaw):
+    # Assume the input in in degree
+    roll = roll / 180 * np.pi
+    pitch = pitch / 180 * np.pi
+    yaw = yaw / 180 * np.pi
+    # Define the rotation matrices
+    Rx = np.array([[1, 0, 0], [0, np.cos(roll), -np.sin(roll)], [0, np.sin(roll), np.cos(roll)]])
+    Ry = np.array([[np.cos(pitch), 0, np.sin(pitch)], [0, 1, 0], [-np.sin(pitch), 0, np.cos(pitch)]])
+    Rz = np.array([[np.cos(yaw), -np.sin(yaw), 0], [np.sin(yaw), np.cos(yaw), 0], [0, 0, 1]])
+    # Combine the rotations
+    R = Rz @ Ry @ Rx
+    return R
+
+
+def rotation_matrix_to_rpy(rotation_matrix):
+    # Get the roll, pitch, yaw in radian
+    roll = np.arctan2(rotation_matrix[2, 1], rotation_matrix[2, 2])
+    pitch = np.arctan2(-rotation_matrix[2, 0], np.sqrt(rotation_matrix[2, 1] ** 2 + rotation_matrix[2, 2] ** 2))
+    yaw = np.arctan2(rotation_matrix[1, 0], rotation_matrix[0, 0])
+    # Get the roll, pitch, yaw in degree
+    roll = roll / np.pi * 180
+    pitch = pitch / np.pi * 180
+    yaw = yaw / np.pi * 180
+    return roll, pitch, yaw
+
+
+def depth2fgpcd(depth, intrinsic_matrix):
+    H, W = depth.shape
+    x, y = np.meshgrid(np.arange(W), np.arange(H))
+    x = x.reshape(-1)
+    y = y.reshape(-1)
+    depth = depth.reshape(-1)
+    points = np.stack([x, y, np.ones_like(x)], axis=1)
+    points = points * depth[:, None]
+    points = points @ np.linalg.inv(intrinsic_matrix).T
+    return points
+
+
+def similarity_transform(from_points, to_points):
+    
+    assert len(from_points.shape) == 2, \
+        "from_points must be a m x n array"
+    assert from_points.shape == to_points.shape, \
+        "from_points and to_points must have the same shape"
+    
+    N, m = from_points.shape
+    
+    mean_from = from_points.mean(axis = 0)
+    mean_to = to_points.mean(axis = 0)
+    
+    delta_from = from_points - mean_from # N x m
+    delta_to = to_points - mean_to       # N x m
+    
+    sigma_from = (delta_from * delta_from).sum(axis = 1).mean()
+    sigma_to = (delta_to * delta_to).sum(axis = 1).mean()
+    
+    cov_matrix = delta_to.T.dot(delta_from) / N
+    
+    U, d, V_t = np.linalg.svd(cov_matrix, full_matrices = True)
+    cov_rank = np.linalg.matrix_rank(cov_matrix)
+    S = np.eye(m)
+    
+    if cov_rank >= m - 1: #  and np.linalg.det(cov_matrix) < 0:  # TODO touch calibration
+        S[m-1, m-1] = -1
+    elif cov_rank < m-1:
+        raise ValueError("colinearility detected in covariance matrix:\n{}".format(cov_matrix))
+    
+    R = U.dot(S).dot(V_t)
+    c = (d * S.diagonal()).sum() / sigma_from
+    t = mean_to - c*R.dot(mean_from)
+    
+    return c, R, t
+
+
+def visualize_o3d(geometries):
+    frame = o3d.geometry.TriangleMesh.create_coordinate_frame(size=0.2, origin=[0, 0, 0])
+    geometries.append(frame)
+
+    pcd = o3d.geometry.PointCloud()
+    pcd.points = o3d.utility.Vector3dVector()
+    viewer = o3d.visualization.Visualizer()
+    viewer.create_window()
+    for geometry in geometries:
+        viewer.add_geometry(geometry)
+    opt = viewer.get_render_option()
+    opt.background_color = np.asarray([1., 1., 1.])
+    viewer.run()
diff --git a/src/experiments/real_world/utils/planning_utils.py b/src/experiments/real_world/utils/planning_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..5f9cd826b076d4ee12716720adcb232166c026e2
--- /dev/null
+++ b/src/experiments/real_world/utils/planning_utils.py
@@ -0,0 +1,40 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import numpy as np
+import math
+
+
+def chamfer(x, y):  # x: (B, N, D), y: (B, M, D)
+    x = x[:, None].repeat(1, y.shape[1], 1, 1)  # (B, M, N, D)
+    y = y[:, :, None].repeat(1, 1, x.shape[2], 1)  # (B, M, N, D)
+    dis = torch.norm(x - y, 2, dim=-1)  # (B, M, N)
+    dis_xy = torch.mean(dis.min(dim=2).values, dim=1)  # dis_xy: mean over N
+    dis_yx = torch.mean(dis.min(dim=1).values, dim=1)  # dis_yx: mean over M
+    return dis_xy + dis_yx
+
+
+def batch_chamfer_dist(xyz, xyz_gt):
+    # xyz: (B, N, 3)
+    # xyz_gt: (M, 3)
+
+    # mean aligning
+    # chamfer = (xyz.mean(dim=1) - xyz_gt.mean(dim=0)).norm(dim=1)  # (B,)
+
+    # chamfer distance
+    xyz_gt = xyz_gt[None]  # (1, M, 3)
+    dist1 = torch.sqrt(torch.sum((xyz[:, :, None] - xyz_gt[:, None]) ** 2, dim=3))  # (B, N, M)
+    dist2 = torch.sqrt(torch.sum((xyz_gt[:, None] - xyz[:, :, None]) ** 2, dim=3))  # (B, M, N)
+    chamfer = torch.mean(torch.min(dist1, dim=1).values, dim=1) + torch.mean(torch.min(dist2, dim=1).values, dim=1)  # (B,)
+    return chamfer
+
+
+def angle_normalize(x):
+    return (((x + math.pi) % (2 * math.pi)) - math.pi)
+
+
+def clip_actions(action, action_lower_lim, action_upper_lim):
+    action_new = action.clone()
+    # action_new[..., 2] = angle_normalize(action[..., 2])
+    action_new.data.clamp_(action_lower_lim, action_upper_lim)
+    return action_new
diff --git a/src/experiments/real_world/utils/render_utils.py b/src/experiments/real_world/utils/render_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..9f04834b7df2eb6bfa20fba0bca9e2bbdbc313cb
--- /dev/null
+++ b/src/experiments/real_world/utils/render_utils.py
@@ -0,0 +1,149 @@
+import torch
+import numpy as np
+import time
+import kornia
+
+
+def interpolate_motions(bones, motions, relations, xyz, rot=None, quat=None, weights=None, device='cuda', step='n/a'):
+    # bones: (n_bones, 3)
+    # motions: (n_bones, 3)
+    # relations: (n_bones, k)
+    # indices: (n_bones,)
+    # xyz: (n_particles, 3)
+    # rot: (n_particles, 3, 3)
+    # quat: (n_particles, 4)
+    # weights: (n_particles, n_bones)
+
+    t0 = time.time()
+    n_bones, _ = bones.shape
+    n_particles, _ = xyz.shape
+
+    # Compute the bone transformations
+    bone_transforms = torch.zeros((n_bones, 4, 4),  device=device)
+
+    n_adj = relations.shape[1]
+    
+    adj_bones = bones[relations] - bones[:, None]  # (n_bones, n_adj, 3)
+    adj_bones_new = (bones[relations] + motions[relations]) - (bones[:, None] + motions[:, None])  # (n_bones, n_adj, 3)
+
+    W = torch.eye(n_adj, device=device)[None].repeat(n_bones, 1, 1)  # (n_bones, n_adj, n_adj)
+
+    # fit a transformation
+    F = adj_bones_new.permute(0, 2, 1) @ W @ adj_bones  # (n_bones, 3, 3)
+    
+    cov_rank = torch.linalg.matrix_rank(F)  # (n_bones,)
+    
+    cov_rank_3_mask = cov_rank == 3  # (n_bones,)
+    cov_rank_2_mask = cov_rank == 2  # (n_bones,)
+    cov_rank_1_mask = cov_rank == 1  # (n_bones,)
+
+    F_2_3 = F[cov_rank_2_mask | cov_rank_3_mask]  # (n_bones, 3, 3)
+    F_1 = F[cov_rank_1_mask]  # (n_bones, 3, 3)
+
+    # 2 or 3
+    try:
+        U, S, V = torch.svd(F_2_3)  # S: (n_bones, 3)
+        S = torch.eye(3, device=device, dtype=torch.float32)[None].repeat(F_2_3.shape[0], 1, 1)
+        neg_det_mask = torch.linalg.det(F_2_3) < 0
+        if neg_det_mask.sum() > 0:
+            print(f'[step {step}] F det < 0 for {neg_det_mask.sum()} bones')
+            S[neg_det_mask, -1, -1] = -1
+        R = U @ S @ V.permute(0, 2, 1)
+    except:
+        print(f'[step {step}] SVD failed')
+        import ipdb; ipdb.set_trace()
+
+    neg_1_det_mask = torch.abs(torch.linalg.det(R) + 1) < 1e-3
+    pos_1_det_mask = torch.abs(torch.linalg.det(R) - 1) < 1e-3
+    bad_det_mask = ~(neg_1_det_mask | pos_1_det_mask)
+
+    if neg_1_det_mask.sum() > 0:
+        print(f'[step {step}] det -1')
+        S[neg_1_det_mask, -1, -1] *= -1
+        R = U @ S @ V.permute(0, 2, 1)
+
+    try:
+        assert bad_det_mask.sum() == 0
+    except:
+        print(f'[step {step}] Bad det')
+        import ipdb; ipdb.set_trace()
+
+    try:
+        if cov_rank_1_mask.sum() > 0:
+            print(f'[step {step}] F rank 1 for {cov_rank_1_mask.sum()} bones')
+            U, S, V = torch.svd(F_1)  # S: (n_bones', 3)
+            assert torch.allclose(S[:, 1:], torch.zeros_like(S[:, 1:]))
+            x = torch.tensor([1., 0., 0.], device=device, dtype=torch.float32)[None].repeat(F_1.shape[0], 1)  # (n_bones', 3)
+            axis = U[:, :, 0]  # (n_bones', 3)
+            perp_axis = torch.linalg.cross(axis, x)  # (n_bones', 3)
+
+            perp_axis_norm_mask = torch.norm(perp_axis, dim=1) < 1e-6
+
+            R = torch.zeros((F_1.shape[0], 3, 3), device=device, dtype=torch.float32)
+            if perp_axis_norm_mask.sum() > 0:
+                print(f'[step {step}] Perp axis norm 0 for {perp_axis_norm_mask.sum()} bones')
+                R[perp_axis_norm_mask] = torch.eye(3, device=device, dtype=torch.float32)[None].repeat(perp_axis_norm_mask.sum(), 1, 1)
+
+            perp_axis = perp_axis[~perp_axis_norm_mask]  # (n_bones', 3)
+            x = x[~perp_axis_norm_mask]  # (n_bones', 3)
+
+            perp_axis = perp_axis / torch.norm(perp_axis, dim=1, keepdim=True)  # (n_bones', 3)
+            third_axis = torch.linalg.cross(x, perp_axis)  # (n_bones', 3)
+            assert ((torch.norm(third_axis, dim=1) - 1).abs() < 1e-6).all()
+            third_axis_after = torch.linalg.cross(axis, perp_axis)  # (n_bones', 3)
+
+            X = torch.stack([x, perp_axis, third_axis], dim=-1)
+            Y = torch.stack([axis, perp_axis, third_axis_after], dim=-1)
+            R[~perp_axis_norm_mask] = Y @ X.permute(0, 2, 1)
+    except:
+        R = torch.zeros((F_1.shape[0], 3, 3), device=device, dtype=torch.float32)
+        R[:, 0, 0] = 1
+        R[:, 1, 1] = 1
+        R[:, 2, 2] = 1
+
+    try:
+        bone_transforms[:, :3, :3] = R
+    except:
+        print(f'[step {step}] Bad R')
+        bone_transforms[:, 0, 0] = 1
+        bone_transforms[:, 1, 1] = 1
+        bone_transforms[:, 2, 2] = 1
+    bone_transforms[:, :3, 3] = motions
+
+    # Compute the weights
+    if weights is None:
+        weights = torch.ones((n_particles, n_bones), device=device)
+        dist = torch.cdist(xyz[None], bones[None])[0]  # (n_particles, n_bones)
+        dist = torch.clamp(dist, min=1e-4)
+        weights = 1 / dist
+        weights = weights / weights.sum(dim=1, keepdim=True)  # (n_particles, n_bones)
+    
+    # Compute the transformed particles
+    xyz_transformed = torch.zeros((n_particles, n_bones, 3), device=device)
+
+    xyz_transformed = xyz[:, None] - bones[None]  # (n_particles, n_bones, 3)
+    xyz_transformed = torch.einsum('ijk,jkl->ijl', xyz_transformed, bone_transforms[:, :3, :3].permute(0, 2, 1))  # (n_particles, n_bones, 3)
+    xyz_transformed = xyz_transformed + bone_transforms[:, :3, 3][None] + bones[None]  # (n_particles, n_bones, 3)
+    xyz_transformed = (xyz_transformed * weights[:, :, None]).sum(dim=1)  # (n_particles, 3)
+
+    def quaternion_multiply(q1, q2):
+        # q1: bsz x 4
+        # q2: bsz x 4
+        q = torch.zeros_like(q1)
+        q[:, 0] = q1[:, 0] * q2[:, 0] - q1[:, 1] * q2[:, 1] - q1[:, 2] * q2[:, 2] - q1[:, 3] * q2[:, 3]
+        q[:, 1] = q1[:, 0] * q2[:, 1] + q1[:, 1] * q2[:, 0] + q1[:, 2] * q2[:, 3] - q1[:, 3] * q2[:, 2]
+        q[:, 2] = q1[:, 0] * q2[:, 2] - q1[:, 1] * q2[:, 3] + q1[:, 2] * q2[:, 0] + q1[:, 3] * q2[:, 1]
+        q[:, 3] = q1[:, 0] * q2[:, 3] + q1[:, 1] * q2[:, 2] - q1[:, 2] * q2[:, 1] + q1[:, 3] * q2[:, 0]
+        return q
+
+    if quat is not None:
+        base_quats = kornia.geometry.conversions.rotation_matrix_to_quaternion(bone_transforms[:, :3, :3])  # (n_bones, 4)
+        base_quats = torch.nn.functional.normalize(base_quats, dim=-1)  # (n_particles, 4)
+        quats = (base_quats[None] * weights[:, :, None]).sum(dim=1)  # (n_particles, 4)
+        quats = torch.nn.functional.normalize(quats, dim=-1)
+        rot = quaternion_multiply(quats, quat)
+
+    # xyz_transformed: (n_particles, 3)
+    # rot: (n_particles, 3, 3) / (n_particles, 4)
+    # weights: (n_particles, n_bones)
+    return xyz_transformed, rot, weights
\ No newline at end of file
diff --git a/src/experiments/real_world/utils/track_utils.py b/src/experiments/real_world/utils/track_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..fa90cd09a1ccd31d2d320b08bb4681a71ca4cde6
--- /dev/null
+++ b/src/experiments/real_world/utils/track_utils.py
@@ -0,0 +1,42 @@
+import numpy as np
+
+
+def sample_points_from_masks(masks, num_points):
+    """
+    sample points from masks and return its absolute coordinates
+
+    Args:
+        masks: np.array with shape (n, h, w)
+        num_points: int
+
+    Returns:
+        points: np.array with shape (n, points, 2)
+    """
+    n, h, w = masks.shape
+    points = []
+
+    for i in range(n):
+        # find the valid mask points
+        indices = np.argwhere(masks[i] == 1)  
+        # the output format of np.argwhere is (y, x) and the shape is (num_points, 2)
+        # we should convert it to (x, y)
+        indices = indices[:, ::-1]  # (num_points, [y x]) to (num_points, [x y])
+        
+        # import pdb; pdb.set_trace()
+        if len(indices) == 0:
+            # if there are no valid points, append an empty array
+            points.append(np.array([]))
+            continue
+        
+        # resampling if there's not enough points
+        if len(indices) < num_points:
+            sampled_indices = np.random.choice(len(indices), num_points, replace=True)
+        else:
+            sampled_indices = np.random.choice(len(indices), num_points, replace=False)
+        
+        sampled_points = indices[sampled_indices]
+        points.append(sampled_points)
+
+    # convert to np.array
+    points = np.array(points, dtype=np.float32)
+    return points
\ No newline at end of file
diff --git a/src/experiments/scripts/train_box.sh b/src/experiments/scripts/train_box.sh
new file mode 100644
index 0000000000000000000000000000000000000000..88c5c97b060b3ca4fdb2e07c18ea7ff9f9bae9f5
--- /dev/null
+++ b/src/experiments/scripts/train_box.sh
@@ -0,0 +1,24 @@
+CUDA_VISIBLE_DEVICES=0 python experiments/train/train_eval.py \
+'gpus=[0]' \
+overwrite=False \
+resume=True \
+debug=True \
+train.name=box/train \
+train.source_dataset_name=data/box_merged/sub_episodes_v \
+train.dataset_name=box/dataset \
+sim.num_grippers=2 \
+sim.gripper_points=True \
+sim.gripper_forcing=False \
+model.material.radius=0.0 \
+train.dataset_load_skip_frame=3 \
+train.dataset_skip_frame=1 \
+train.num_workers=8 \
+train.batch_size=32 \
+train.training_start_episode=0 \
+train.training_end_episode=303 \
+train.eval_start_episode=303 \
+train.eval_end_episode=323 \
+train.num_iterations=100000 \
+train.iteration_eval_interval=1000 \
+train.iteration_log_interval=10 \
+train.iteration_save_interval=1000
diff --git a/src/experiments/scripts/train_bread.sh b/src/experiments/scripts/train_bread.sh
new file mode 100644
index 0000000000000000000000000000000000000000..1272540bd208d571728b27c9be072fddb4a520c0
--- /dev/null
+++ b/src/experiments/scripts/train_bread.sh
@@ -0,0 +1,27 @@
+CUDA_VISIBLE_DEVICES=0 python experiments/train/train_eval.py \
+'gpus=[0]' \
+overwrite=False \
+resume=True \
+debug=True \
+train.name=bread/train \
+train.source_dataset_name=data/bread_merged/sub_episodes_v \
+train.dataset_name=bread/dataset \
+sim.preprocess_scale=3.0 \
+sim.num_grippers=2 \
+sim.n_particles=500 \
+'sim.num_grids=[100, 100, 100, 0.01]' \
+model.gripper_radius=0.12 \
+model.material.radius=0.0 \
+model.material.pe_num_func_res=4 \
+train.dataset_load_skip_frame=3 \
+train.dataset_skip_frame=1 \
+train.num_workers=8 \
+train.batch_size=16 \
+train.training_start_episode=0 \
+train.training_end_episode=143 \
+train.eval_start_episode=143 \
+train.eval_end_episode=163 \
+train.num_iterations=100000 \
+train.iteration_eval_interval=1000 \
+train.iteration_log_interval=10 \
+train.iteration_save_interval=1000 
diff --git a/src/experiments/scripts/train_cloth.sh b/src/experiments/scripts/train_cloth.sh
new file mode 100644
index 0000000000000000000000000000000000000000..3305f9de7b724b8ef71bbf76811e92ff3ac92a8f
--- /dev/null
+++ b/src/experiments/scripts/train_cloth.sh
@@ -0,0 +1,22 @@
+CUDA_VISIBLE_DEVICES=0 python experiments/train/train_eval.py \
+'gpus=[0]' \
+overwrite=False \
+resume=True \
+debug=True \
+train.name=cloth/train \
+train.source_dataset_name=data/cloth_merged/sub_episodes_v \
+train.dataset_name=cloth/dataset \
+sim.preprocess_scale=0.8 \
+sim.num_grippers=2 \
+train.dataset_load_skip_frame=3 \
+train.dataset_skip_frame=1 \
+train.num_workers=8 \
+train.batch_size=32 \
+train.training_start_episode=0 \
+train.training_end_episode=610 \
+train.eval_start_episode=610 \
+train.eval_end_episode=650 \
+train.num_iterations=100000 \
+train.iteration_eval_interval=1000 \
+train.iteration_log_interval=10 \
+train.iteration_save_interval=1000 
diff --git a/src/experiments/scripts/train_paperbag.sh b/src/experiments/scripts/train_paperbag.sh
new file mode 100644
index 0000000000000000000000000000000000000000..c7d6ccb9be0fe83a3ff5cca6bb1bd61f0f4b57c4
--- /dev/null
+++ b/src/experiments/scripts/train_paperbag.sh
@@ -0,0 +1,20 @@
+CUDA_VISIBLE_DEVICES=0 python experiments/train/train_eval.py \
+'gpus=[0]' \
+overwrite=False \
+resume=True \
+debug=True \
+train.name=paperbag/train \
+train.source_dataset_name=data/paperbag_merged/sub_episodes_v \
+train.dataset_name=paperbag/dataset \
+train.dataset_load_skip_frame=3 \
+train.dataset_skip_frame=1 \
+train.num_workers=8 \
+train.batch_size=32 \
+train.training_start_episode=0 \
+train.training_end_episode=200 \
+train.eval_start_episode=200 \
+train.eval_end_episode=220 \
+train.num_iterations=100000 \
+train.iteration_eval_interval=1000 \
+train.iteration_log_interval=10 \
+train.iteration_save_interval=1000 
diff --git a/src/experiments/scripts/train_rope.sh b/src/experiments/scripts/train_rope.sh
new file mode 100644
index 0000000000000000000000000000000000000000..7e32c2200bcac0564a1f62e7c1c1f28b7c1d5728
--- /dev/null
+++ b/src/experiments/scripts/train_rope.sh
@@ -0,0 +1,20 @@
+CUDA_VISIBLE_DEVICES=0 python experiments/train/train_eval.py \
+'gpus=[0]' \
+overwrite=False \
+resume=True \
+debug=True \
+train.name=rope/train \
+train.source_dataset_name=data/rope_merged/sub_episodes_v \
+train.dataset_name=rope/dataset \
+train.dataset_load_skip_frame=3 \
+train.dataset_skip_frame=1 \
+train.num_workers=8 \
+train.batch_size=32 \
+train.training_start_episode=0 \
+train.training_end_episode=651 \
+train.eval_start_episode=651 \
+train.eval_end_episode=691 \
+train.num_iterations=100000 \
+train.iteration_eval_interval=1000 \
+train.iteration_log_interval=10 \
+train.iteration_save_interval=1000 
diff --git a/src/experiments/scripts/train_sloth.sh b/src/experiments/scripts/train_sloth.sh
new file mode 100644
index 0000000000000000000000000000000000000000..22eabd5a769a24d5131ab82b1e2b29266ac9a725
--- /dev/null
+++ b/src/experiments/scripts/train_sloth.sh
@@ -0,0 +1,20 @@
+CUDA_VISIBLE_DEVICES=0 python experiments/train/train_eval.py \
+'gpus=[0]' \
+overwrite=False \
+resume=True \
+debug=True \
+train.name=sloth/train \
+train.source_dataset_name=data/sloth_merged/sub_episodes_v \
+train.dataset_name=sloth/dataset \
+train.dataset_load_skip_frame=3 \
+train.dataset_skip_frame=1 \
+train.num_workers=8 \
+train.batch_size=32 \
+train.training_start_episode=0 \
+train.training_end_episode=113 \
+train.eval_start_episode=113 \
+train.eval_end_episode=133 \
+train.num_iterations=100000 \
+train.iteration_eval_interval=1000 \
+train.iteration_log_interval=10 \
+train.iteration_save_interval=1000 
diff --git a/src/experiments/train/__init__.py b/src/experiments/train/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/src/experiments/train/eval.py b/src/experiments/train/eval.py
new file mode 100644
index 0000000000000000000000000000000000000000..779571bf807b5436ddfce8a72224aacb82718b8a
--- /dev/null
+++ b/src/experiments/train/eval.py
@@ -0,0 +1,458 @@
+from pathlib import Path
+import random
+from tqdm import tqdm, trange
+
+import argparse
+import yaml
+import hydra
+from omegaconf import DictConfig, OmegaConf
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.backends.cudnn
+import warp as wp
+import glob
+from torch.utils.data import DataLoader
+import os
+import matplotlib.pyplot as plt
+import json
+import sys
+sys.path.append(str(Path(__file__).parent.parent.parent))
+sys.path.append(str(Path(__file__).parent.parent))
+
+from pgnd.sim import Friction, CacheDiffSimWithFrictionBatch, StaticsBatch, CollidersBatch
+from pgnd.material import PGNDModel
+from pgnd.data import RealTeleopBatchDataset, RealGripperDataset
+from pgnd.utils import Logger, get_root, mkdir
+
+from gs import do_gs
+from pv_train import do_train_pv
+from pv_dataset import do_dataset_pv
+from metric_eval import do_metric
+from train_eval import transform_gripper_points, dataloader_wrapper
+
+root: Path = get_root(__file__)
+
+
+def eval(
+    cfg: DictConfig,
+    ckpt_path: str,
+    episode: int,
+    dataset_pv: bool = True,
+    eval_base_name: str = 'eval-val',
+    use_pv: bool = True,
+    use_gs: bool = True,
+):
+
+    # init
+    wp.init()
+    wp.ScopedTimer.enabled = False
+    wp.set_module_options({'fast_math': False})
+
+    gpus = [int(gpu) for gpu in cfg.gpus]
+    wp_devices = [wp.get_device(f'cuda:{gpu}') for gpu in gpus]
+    torch_devices = [torch.device(f'cuda:{gpu}') for gpu in gpus]
+    device_count = len(torch_devices)
+    
+    assert device_count == 1
+    wp_device = wp_devices[0]
+    torch_device = torch_devices[0]
+
+    seed = cfg.seed
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+
+    torch.autograd.set_detect_anomaly(True)
+
+    torch.backends.cudnn.benchmark = True
+
+    log_root: Path = root / 'log'
+    eval_name = f'{cfg.train.name}/{eval_base_name}/{cfg.train.dataset_name.split("/")[-1]}/{cfg.iteration:06d}'
+    exp_root: Path = log_root / eval_name
+    state_root: Path = exp_root / 'state'
+    mkdir(state_root, overwrite=cfg.overwrite, resume=cfg.resume)
+    episode_state_root = state_root / f'episode_{episode:04d}'
+    mkdir(episode_state_root, overwrite=cfg.overwrite, resume=cfg.resume)
+    OmegaConf.save(cfg, exp_root / 'hydra.yaml', resolve=True)
+
+    use_pv = cfg.train.use_pv
+    if not use_pv:
+        print('not using pv rendering...')
+
+    # decide whether to use gs rendering based on the existence of gs files
+    assert os.path.exists(log_root / str(cfg.train.source_dataset_name) / f'episode_{episode:04d}' / 'meta.txt')
+    meta = np.loadtxt(log_root / str(cfg.train.source_dataset_name) / f'episode_{episode:04d}' / 'meta.txt')
+    with open(log_root / str(cfg.train.source_dataset_name) / 'metadata.json') as f:
+        datadir_list = json.load(f)
+    datadir = datadir_list[episode]
+    source_data_dir = datadir['path']
+    source_episode_id = int(meta[0])
+    source_frame_start = int(meta[1]) + int(cfg.sim.n_history) * int(cfg.train.dataset_load_skip_frame) * int(cfg.train.dataset_skip_frame)
+    source_frame_end = int(meta[2])
+    if use_gs:
+        use_gs = os.path.exists((log_root.parent.parent / source_data_dir).parent / f'episode_{source_episode_id:04d}' / 'gs' / f'{source_frame_start:06d}.splat')
+
+    if cfg.train.dataset_name is None:
+        cfg.train.dataset_name = Path(cfg.train.name).parent / 'dataset'
+    assert cfg.train.source_dataset_name is not None
+
+    source_dataset_root = log_root / str(cfg.train.source_dataset_name)
+    assert os.path.exists(source_dataset_root)
+
+    dataset = RealTeleopBatchDataset(
+        cfg, 
+        dataset_root=log_root / cfg.train.dataset_name / 'state',
+        source_data_root=source_dataset_root,
+        device=torch_device,
+        num_steps=cfg.sim.num_steps,
+        eval_episode_name=f'episode_{episode:04d}',
+    )
+    dataloader = dataloader_wrapper(
+        DataLoader(dataset, batch_size=1, shuffle=False, num_workers=cfg.train.num_workers, pin_memory=True),
+        'dataset'
+    )
+    if cfg.sim.gripper_points:
+        eval_gripper_dataset = RealGripperDataset(
+            cfg,
+            device=torch_device,
+        )
+        eval_gripper_dataloader = dataloader_wrapper(
+            DataLoader(eval_gripper_dataset, batch_size=1, shuffle=False, num_workers=cfg.train.num_workers, pin_memory=True),
+            'gripper_dataset'
+        )
+
+    # load ckpt
+    if ckpt_path is None:
+        if cfg.model.ckpt is not None:
+            ckpt_path = cfg.model.ckpt
+        else:
+            ckpt_path = log_root / cfg.train.name / 'ckpt' / f'{cfg.iteration:06d}.pt'
+    ckpt = torch.load(log_root / ckpt_path, map_location=torch_device)
+    
+    material: nn.Module = PGNDModel(cfg)
+    material.to(torch_device)
+    material.load_state_dict(ckpt['material'])
+    material.requires_grad_(False)
+    material.eval()
+
+    if 'friction' in ckpt:
+        friction = ckpt['friction']['mu'].reshape(-1, 1)
+    else:
+        friction = torch.tensor(cfg.model.friction.value, device=torch_device).reshape(-1, 1)
+
+    init_state, actions, gt_states, downsample_indices = next(dataloader)
+
+    x, v, x_his, v_his, clip_bound, enabled, episode_vec = init_state
+    x = x.to(torch_device)
+    v = v.to(torch_device)
+    x_his = x_his.to(torch_device)
+    v_his = v_his.to(torch_device)
+
+    actions = actions.to(torch_device)
+
+    if cfg.sim.gripper_points:
+        gripper_points, _ = next(eval_gripper_dataloader)
+        gripper_points = gripper_points.to(torch_device)
+        gripper_x, gripper_v, gripper_mask = transform_gripper_points(cfg, gripper_points, actions)  # (bsz, num_steps, num_grippers, 3)
+
+    gt_x, gt_v = gt_states
+    gt_x = gt_x.to(torch_device)
+    gt_v = gt_v.to(torch_device)
+
+    # gt_states: (bsz, num_steps_total)
+    batch_size = gt_x.shape[0]
+    num_steps_total = gt_x.shape[1]
+    num_particles = gt_x.shape[2]
+    assert batch_size == 1
+
+    if cfg.sim.gripper_points:
+        num_gripper_particles = gripper_x.shape[2]
+        num_particles_orig = num_particles
+        num_particles = num_particles + num_gripper_particles
+
+    cfg.sim.num_steps = num_steps_total
+    sim = CacheDiffSimWithFrictionBatch(cfg, num_steps_total, batch_size, wp_device, requires_grad=True)
+
+    statics = StaticsBatch()
+    statics.init(shape=(batch_size, num_particles), device=wp_device)
+    statics.update_clip_bound(clip_bound)
+    statics.update_enabled(enabled)
+    colliders = CollidersBatch()
+
+    if cfg.sim.gripper_points:
+        assert not cfg.sim.gripper_forcing
+        num_grippers = 0
+    else:
+        num_grippers = cfg.sim.num_grippers
+
+    colliders.init(shape=(batch_size, num_grippers), device=wp_device)
+    if num_grippers > 0:
+        assert len(actions.shape) > 2
+        colliders.initialize_grippers(actions[:, 0])
+
+    colliders_save = colliders.export()
+    colliders_save = {key: torch.from_numpy(colliders_save[key])[0].to(x.device).to(x.dtype) for key in colliders_save}
+    ckpt = dict(x=x[0], v=v[0], **colliders_save)
+
+    torch.save(ckpt, episode_state_root / f'{0:04d}.pt')
+
+    enabled = enabled.to(torch_device)  # (bsz, num_particles)
+    enabled_mask = enabled.unsqueeze(-1).repeat(1, 1, 3)  # (bsz, num_particles, 3)
+
+    losses = {}
+    with torch.no_grad():
+        for step in trange(num_steps_total):
+            if num_grippers > 0:
+                colliders.update_grippers(actions[:, step])
+            if cfg.sim.gripper_forcing:
+                x_in = x.clone()
+            else:
+                x_in = None
+
+            if cfg.sim.gripper_points:
+                x = torch.cat([x, gripper_x[:, step]], dim=1)  # gripper_points: (bsz, num_steps, num_particles, 3)
+                v = torch.cat([v, gripper_v[:, step]], dim=1)
+                x_his = torch.cat([x_his, torch.zeros((gripper_x.shape[0], gripper_x.shape[2], cfg.sim.n_history * 3), device=x_his.device, dtype=x_his.dtype)], dim=1)
+                v_his = torch.cat([v_his, torch.zeros((gripper_x.shape[0], gripper_x.shape[2], cfg.sim.n_history * 3), device=v_his.device, dtype=v_his.dtype)], dim=1)
+                if enabled.shape[1] < num_particles:
+                    enabled = torch.cat([enabled, gripper_mask[:, step]], dim=1)
+                statics.update_enabled(enabled.cpu())
+
+            pred = material(x, v, x_his, v_his, enabled)
+
+            if pred.isnan().any():
+                print('pred isnan', pred.min().item(), pred.max().item())
+                break
+            if pred.isinf().any():
+                print('pred isinf', pred.min().item(), pred.max().item())
+                break
+
+            x, v = sim(statics, colliders, step, x, v, friction, pred)
+
+            if cfg.sim.gripper_forcing:
+                assert not cfg.sim.gripper_points
+                gripper_xyz = actions[:, step, :, :3]
+                gripper_v = actions[:, step, :, 3:6]
+                x_from_gripper = x_in[:, None] - gripper_xyz[:, :, None]  # (bsz, num_grippers, num_particles, 3)
+                x_gripper_distance = torch.norm(x_from_gripper, dim=-1)  # (bsz, num_grippers, num_particles)
+                x_gripper_distance_mask = x_gripper_distance < cfg.model.gripper_radius
+                x_gripper_distance_mask = x_gripper_distance_mask.unsqueeze(-1).repeat(1, 1, 1, 3)  # (bsz, num_grippers, num_particles, 3)
+                gripper_v_expand = gripper_v[:, :, None].repeat(1, 1, num_particles, 1)  # (bsz, num_grippers, num_particles, 3)
+
+                gripper_closed = actions[:, step, :, -1] < 0.5  # (bsz, num_grippers)  # 1: open, 0: close
+                x_gripper_distance_mask = torch.logical_and(x_gripper_distance_mask, gripper_closed[:, :, None, None].repeat(1, 1, num_particles, 3))
+
+                gripper_quat_vel = actions[:, step, :, 10:13]  # (bsz, num_grippers, 3)
+                gripper_angular_vel = torch.linalg.norm(gripper_quat_vel, dim=-1, keepdims=True)  # (bsz, num_grippers, 1)
+                gripper_quat_axis = gripper_quat_vel / (gripper_angular_vel + 1e-10)  # (bsz, num_grippers, 3)
+
+                grid_from_gripper_axis = x_from_gripper - \
+                    (gripper_quat_axis[:, :, None] * x_from_gripper).sum(dim=-1, keepdims=True) * gripper_quat_axis[:, :, None]  # (bsz, num_grippers, num_particles, 3)
+                gripper_v_expand = torch.cross(gripper_quat_vel[:, :, None], grid_from_gripper_axis, dim=-1) + gripper_v_expand
+
+                for i in range(gripper_xyz.shape[1]):
+                    x_gripper_distance_mask_single = x_gripper_distance_mask[:, i]
+                    x[x_gripper_distance_mask_single] = x_in[x_gripper_distance_mask_single] + cfg.sim.dt * gripper_v_expand[:, i][x_gripper_distance_mask_single]
+                    v[x_gripper_distance_mask_single] = gripper_v_expand[:, i][x_gripper_distance_mask_single]
+            
+            if cfg.sim.n_history > 0:
+                if cfg.sim.gripper_points:
+                    x_his_particles = torch.cat([x_his[:, :num_particles_orig].reshape(batch_size, num_particles_orig, -1, 3)[:, :, 1:], x[:, :num_particles_orig, None].detach()], dim=2)
+                    v_his_particles = torch.cat([v_his[:, :num_particles_orig].reshape(batch_size, num_particles_orig, -1, 3)[:, :, 1:], v[:, :num_particles_orig, None].detach()], dim=2)
+                    x_his = x_his_particles.reshape(batch_size, num_particles_orig, -1)
+                    v_his = v_his_particles.reshape(batch_size, num_particles_orig, -1)
+                else:
+                    x_his = torch.cat([x_his.reshape(batch_size, num_particles, -1, 3)[:, :, 1:], x[:, :, None].detach()], dim=2)
+                    v_his = torch.cat([v_his.reshape(batch_size, num_particles, -1, 3)[:, :, 1:], v[:, :, None].detach()], dim=2)
+                    x_his = x_his.reshape(batch_size, num_particles, -1)
+                    v_his = v_his.reshape(batch_size, num_particles, -1)
+
+            if cfg.sim.gripper_points:
+                extra_save = {
+                    'gripper_x': gripper_x[0, step],
+                    'gripper_v': gripper_v[0, step],
+                    'gripper_actions': actions[0, step],
+                }
+                x = x[:, :num_particles_orig]
+                v = v[:, :num_particles_orig]
+                enabled = enabled[:, :num_particles_orig]
+            else:
+                extra_save = {}
+
+            colliders_save = colliders.export()
+            colliders_save = {key: torch.from_numpy(colliders_save[key])[0].to(x.device).to(x.dtype) for key in colliders_save}
+            
+            loss_x = nn.functional.mse_loss(x[enabled_mask > 0], gt_x[:, step][enabled_mask > 0])
+            loss_v = nn.functional.mse_loss(v[enabled_mask > 0], gt_v[:, step][enabled_mask > 0])
+            losses[step] = dict(loss_x=loss_x.item(), loss_v=loss_v.item())
+            
+            ckpt = dict(x=x[0], v=v[0], **colliders_save, **extra_save)
+
+            if step % cfg.sim.skip_frame == 0:
+                torch.save(ckpt, episode_state_root / f'{int(step / cfg.sim.skip_frame):04d}.pt')
+
+    for loss_k in losses[0].keys():
+        plt.figure(figsize=(10, 5))
+        loss_list = [losses[step][loss_k] for step in losses]
+        plt.plot(loss_list)
+        plt.title(loss_k)
+        plt.grid()
+        plt.savefig(state_root / f'episode_{episode:04d}_{loss_k}.png', dpi=300)
+
+    ## pv
+    if use_pv:
+        do_train_pv(
+            cfg,
+            log_root,
+            cfg.iteration,
+            [f'episode_{episode:04d}'],
+            eval_dirname=eval_base_name,
+            dataset_name=cfg.train.dataset_name.split("/")[-1],
+            eval_postfix='',
+        )
+
+    if use_gs:
+        do_gs(
+            cfg,
+            log_root,
+            cfg.iteration,
+            [f'episode_{episode:04d}'],
+            eval_dirname=eval_base_name,
+            dataset_name=cfg.train.dataset_name.split("/")[-1],
+            eval_postfix='',
+            camera_id=1,
+            with_mask=True,
+            with_bg=True,
+        )
+
+    if use_pv:
+        save_dir = log_root / f'{cfg.train.name}/{eval_base_name}/{cfg.train.dataset_name.split("/")[-1]}/{cfg.iteration:06d}/pv'
+        _ = do_dataset_pv(
+            cfg,
+            log_root / str(cfg.train.dataset_name),
+            [f'episode_{episode:04d}'],
+            save_dir=save_dir,
+            downsample_indices=downsample_indices,
+        )
+    
+    metrics = do_metric(
+        cfg,
+        log_root,
+        cfg.iteration,
+        [f'episode_{episode:04d}'],
+        downsample_indices,
+        eval_dirname=eval_base_name,
+        dataset_name=cfg.train.dataset_name.split("/")[-1],
+        eval_postfix='',
+        camera_id=1,
+        use_gs=use_gs,
+    )
+    return metrics
+
+
+@torch.no_grad()
+def main(
+    cfg: DictConfig, 
+):
+
+    print(OmegaConf.to_yaml(cfg, resolve=True))
+
+    metrics_list = []
+    for episode in range(cfg.start_episode, cfg.end_episode):
+        if "eval_state_only" in cfg and cfg.eval_state_only:
+            use_pv = False
+            use_gs = False
+            eval_base_name = 'eval_state'
+        else:
+            use_pv = True
+            use_gs = True
+            eval_base_name = 'eval'
+        metrics = eval(cfg,
+            None,
+            episode,
+            dataset_pv=True,
+            eval_base_name=eval_base_name,
+            use_pv=use_pv, 
+            use_gs=use_gs,
+        )
+        metrics_list.append(metrics)
+
+    metrics_list = np.array(metrics_list)[:, 0]
+
+    if metrics_list.shape[-1] == 10:
+        metric_names = ['mse', 'chamfer', 'emd', 'jscore', 'fscore', 'jfscore', 'perception', 'psnr', 'ssim', 'iou']
+    else:
+        assert metrics_list.shape[-1] == 3
+        metric_names = ['mse', 'chamfer', 'emd']
+
+    median_metric = np.median(metrics_list, axis=0)
+    step_75_metric = np.percentile(metrics_list, 75, axis=0)
+    step_25_metric = np.percentile(metrics_list, 25, axis=0)
+
+    for i, metric_name in enumerate(metric_names):
+        # plot error
+        x = np.arange(1, len(median_metric) + 1)
+        plt.figure(figsize=(10, 5))
+        plt.plot(x, median_metric[:, i])
+        plt.xlabel(f"prediction steps, dt={cfg.sim.dt}")
+        plt.ylabel(metric_name)
+        plt.grid()
+
+        ax = plt.gca()
+        x = np.arange(1, len(median_metric) + 1)
+        ax.fill_between(x, step_25_metric[:, i], step_75_metric[:, i], alpha=0.2)
+
+        save_dir = root / 'log' / cfg.train.name / eval_base_name / cfg.train.dataset_name.split("/")[-1] / f'{cfg.iteration:06d}' / 'metric'
+        plt.savefig(os.path.join(save_dir, f'{i:02d}-{metric_name}.png'))
+        plt.close()
+
+    mean_metric = np.mean(metrics_list, axis=0)
+    std_metric = np.std(metrics_list, axis=0)
+
+    n_steps = 30
+    mean_metric_step = mean_metric[n_steps]
+    std_metric_step = std_metric[n_steps]
+
+    if mean_metric.shape[-1] == 10:
+        mse, chamfer, emd, jscore, fscore, jfscore, perception, psnr, ssim, iou = mean_metric_step
+        mse_std, chamfer_std, emd_std, jscore_std, fscore_std, jfscore_std, perception_std, psnr_std, ssim_std, iou_std = std_metric_step
+        print(f'3D MSE: {mse:.4f} {mse_std:.4f}, 3D CD: {chamfer:.4f} {chamfer_std:.4f}, 3D EMD: {emd:.4f} {emd_std:.4f}', end=' ')
+        print(f'J-Score: {jscore:.4f} {jscore_std:.4f}, F-Score: {fscore:.4f} {fscore_std:.4f}, JF-Score: {jfscore:.4f} {jfscore_std:.4f}', end=' ')
+        print(f'perception: {perception:.4f} {perception_std:.4f}, PSNR: {psnr:.4f} {psnr_std:.4f}, SSIM: {ssim:.4f} {ssim_std:.4f}, IoU: {iou:.4f} {iou_std:.4f}')
+    else:
+        mse, chamfer, emd = mean_metric_step
+        mse_std, chamfer_std, emd_std = std_metric_step
+        print(f'3D MSE: {mse:.4f} {mse_std:.4f}, 3D CD: {chamfer:.4f} {chamfer_std:.4f}, 3D EMD: {emd:.4f} {emd_std:.4f}')
+
+
+if __name__ == '__main__':
+
+    best_models = {
+        'cloth': ['cloth', 'train', 100000, [610, 650]],
+        'rope': ['rope', 'train', 100000, [651, 691]],
+        'paperbag': ['paperbag', 'train', 100000, [200, 220]],
+        'sloth': ['sloth', 'train', 100000, [113, 133]],
+        'box': ['box', 'train', 100000, [306, 323]],
+        'bread': ['bread', 'train', 100000, [143, 163]],
+    }
+
+    arg_parser = argparse.ArgumentParser()
+    arg_parser.add_argument('--task', type=str, required=True)
+    arg_parser.add_argument('--state_only', action='store_true')
+    args = arg_parser.parse_args()
+
+    with open(root / f'log/{best_models[args.task][0]}/{best_models[args.task][1]}/hydra.yaml', 'r') as f:
+        config = yaml.load(f, Loader=yaml.CLoader)
+    cfg = OmegaConf.create(config)
+
+    cfg.iteration = best_models[args.task][2]
+    cfg.start_episode = best_models[args.task][3][0]
+    cfg.end_episode = best_models[args.task][3][1]
+    cfg.sim.num_steps = 1000
+    cfg.sim.gripper_forcing = False
+    cfg.sim.uniform = True
+    cfg.sim.use_pv = True
+    cfg.eval_state_only = args.state_only
+
+    main(cfg)
diff --git a/src/experiments/train/gs.py b/src/experiments/train/gs.py
new file mode 100644
index 0000000000000000000000000000000000000000..6d99b4298316d093ebd31d6536b3fbd6fafa91a3
--- /dev/null
+++ b/src/experiments/train/gs.py
@@ -0,0 +1,526 @@
+from pathlib import Path
+import random
+from tqdm import tqdm, trange
+import hydra
+from omegaconf import DictConfig, OmegaConf
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.backends.cudnn
+import math
+import os
+import cv2
+from sklearn.neighbors import NearestNeighbors
+import json
+import kornia
+
+from pgnd.utils import get_root, mkdir
+from pgnd.ffmpeg import make_video
+
+from real_world.utils.render_utils import interpolate_motions
+from real_world.gs.helpers import setup_camera
+from real_world.gs.convert import save_to_splat, read_splat
+
+from diff_gaussian_rasterization import GaussianRasterizer
+from diff_gaussian_rasterization import GaussianRasterizationSettings as Camera
+
+root: Path = get_root(__file__)
+
+
+def Rt_to_w2c(R, t):
+    c2w = np.concatenate([np.concatenate([R, t.reshape(3, 1)], axis=1), np.array([[0, 0, 0, 1]])], axis=0)
+    w2c = np.linalg.inv(c2w)
+    return w2c
+
+
+class GSRenderer:
+    def __init__(self, cfg, device='cuda'):
+        self.cfg = cfg
+        self.device = device
+        self.k_rel = 16  # knn for relations
+        self.k_wgt = 16  # knn for weights
+        self.clear()
+    
+    def clear(self, clear_params=True):
+        self.metadata = None
+        self.config = None
+        if clear_params:
+            self.params = None
+    
+    def load_params(self, params_path, remove_low_opa=True, remove_black=False):
+        pts, colors, scales, quats, opacities = read_splat(params_path)
+        
+        if remove_low_opa:
+            low_opa_idx = opacities[:, 0] < 0.1
+            pts = pts[~low_opa_idx]
+            colors = colors[~low_opa_idx]
+            quats = quats[~low_opa_idx]
+            opacities = opacities[~low_opa_idx]
+            scales = scales[~low_opa_idx]
+
+        if remove_black:
+            low_color_idx = colors.sum(axis=-1) < 0.5
+            pts = pts[~low_color_idx]
+            colors = colors[~low_color_idx]
+            quats = quats[~low_color_idx]
+            opacities = opacities[~low_color_idx]
+            scales = scales[~low_color_idx]
+        
+        self.params = {
+            'means3D': torch.from_numpy(pts).to(self.device),
+            'rgb_colors': torch.from_numpy(colors).to(self.device),
+            'log_scales': torch.log(torch.from_numpy(scales).to(self.device)),
+            'unnorm_rotations': torch.from_numpy(quats).to(self.device),
+            'logit_opacities': torch.logit(torch.from_numpy(opacities).to(self.device))
+        }
+
+        gripper_splat = root / 'log/gs/ckpts/gripper.splat'  # gripper_new.splat
+        table_splat = root / 'log/gs/ckpts/table.splat'
+
+        self.gripper_params = read_splat(gripper_splat)
+        self.table_params = read_splat(table_splat)
+
+    def set_camera(self, w, h, intr, w2c=None, R=None, t=None, near=0.01, far=100.0):
+        if w2c is None:
+            assert R is not None and t is not None
+            w2c = Rt_to_w2c(R, t)
+        self.metadata = {
+            'w': w,
+            'h': h,
+            'k': intr,
+            'w2c': w2c,
+        }
+        self.config = {'near': near, 'far': far}
+
+    @torch.no_grad
+    def render(self, render_data, cam_id, bg=[0, 0, 0]):
+        render_data = {k: v.to(self.device) for k, v in render_data.items()}
+        w, h = self.metadata['w'], self.metadata['h']
+        k, w2c = self.metadata['k'], self.metadata['w2c']
+        cam = setup_camera(w, h, k, w2c, self.config['near'], self.config['far'], bg)
+        im, _, depth, = GaussianRasterizer(raster_settings=cam)(**render_data)
+        return im, depth
+    
+    def knn_relations(self, bones):
+        k = self.k_rel
+        knn = NearestNeighbors(n_neighbors=k+1, algorithm='kd_tree').fit(bones.detach().cpu().numpy())
+        _, indices = knn.kneighbors(bones.detach().cpu().numpy())  # (N, k)
+        indices = indices[:, 1:]  # exclude self
+        return indices
+    
+    def knn_weights(self, bones, pts):
+        k = self.k_wgt
+        knn = NearestNeighbors(n_neighbors=k, algorithm='kd_tree').fit(bones.detach().cpu().numpy())
+        _, indices = knn.kneighbors(pts.detach().cpu().numpy())
+        bones_selected = bones[indices]  # (N, k, 3)
+        dist = torch.norm(bones_selected - pts[:, None], dim=-1)  # (N, k)
+        weights = 1 / (dist + 1e-6)
+        weights = weights / weights.sum(dim=-1, keepdim=True)  # (N, k)
+        weights_all = torch.zeros((pts.shape[0], bones.shape[0]), device=pts.device)
+        weights_all[torch.arange(pts.shape[0])[:, None], indices] = weights
+        return weights_all
+
+    def rollout_and_render(self, pts_list, grippers=[], with_bg=False):
+        assert self.params is not None
+
+        pts_list = pts_list.to(self.device)
+
+        if grippers != []:
+            n_grippers = grippers.shape[1]
+            grippers = grippers.to(self.device)
+            gripper_center = grippers[:, :, :3]
+            gripper_quat = grippers[:, :, 6:10]
+            gripper_radius = grippers[:, :, 13]
+
+        xyz_0 = self.params['means3D']
+        rgb_0 = self.params['rgb_colors']
+        quat_0 = torch.nn.functional.normalize(self.params['unnorm_rotations'])
+        opa_0 = torch.sigmoid(self.params['logit_opacities'])
+        scales_0 = torch.exp(self.params['log_scales'])
+
+        pts_prev = pts_list[0]
+
+        xyz_list = [xyz_0]
+        rgb_list = [rgb_0]
+        quat_list = [quat_0]
+        opa_list = [opa_0]
+        scales_list = [scales_0]
+        for i in range(1, len(pts_list)):
+            pts = pts_list[i]
+
+            xyz, quat, _ = interpolate_motions(
+                bones=pts_prev,
+                motions=pts - pts_prev,
+                relations=self.knn_relations(pts_prev),
+                weights=self.knn_weights(pts_prev, xyz_list[-1]),
+                xyz=xyz_list[-1],
+                quat=quat_list[-1],
+                step=f'{i-1}->{i}'
+            )
+
+            pts_prev = pts
+            xyz_list.append(xyz)
+            quat_list.append(quat)
+            rgb_list.append(rgb_list[-1])
+            opa_list.append(opa_list[-1])
+            scales_list.append(scales_list[-1])
+        
+        n_steps = len(xyz_list)
+        xyz = torch.stack(xyz_list, dim=0).to(torch.float32)
+        rgb = torch.stack(rgb_list, dim=0).to(torch.float32)
+        quat = torch.stack(quat_list, dim=0).to(torch.float32)
+        opa = torch.stack(opa_list, dim=0).to(torch.float32)
+        scales = torch.stack(scales_list, dim=0).to(torch.float32)
+
+        # interpolate smoothly
+        change_points = (xyz - torch.concatenate([xyz[0:1], xyz[:-1]], dim=0)).norm(dim=-1).sum(dim=-1).nonzero().squeeze(1)
+        change_points = torch.cat([torch.tensor([0]).to(change_points.device), change_points])
+        for i in range(1, len(change_points)):
+            start = change_points[i - 1]
+            end = change_points[i]
+            if end - start < 2:  # gap is 0 or 1
+                continue
+            xyz[start:end] = torch.lerp(xyz[start][None], xyz[end][None], torch.linspace(0, 1, end - start + 1).to(xyz.device)[:, None, None])[:-1]
+            rgb[start:end] = torch.lerp(rgb[start][None], rgb[end][None], torch.linspace(0, 1, end - start + 1).to(rgb.device)[:, None, None])[:-1]
+            quat[start:end] = torch.lerp(quat[start][None], quat[end][None], torch.linspace(0, 1, end - start + 1).to(quat.device)[:, None, None])[:-1]
+            opa[start:end] = torch.lerp(opa[start][None], opa[end][None], torch.linspace(0, 1, end - start + 1).to(opa.device)[:, None, None])[:-1]
+
+        quat = torch.nn.functional.normalize(quat, dim=-1)
+        mean_xyz = xyz.mean((0, 1))
+
+        if with_bg:
+            ## add table and gripper
+            # add table
+            t_pts, t_colors, t_scales, t_quats, t_opacities = self.table_params
+            t_pts = torch.tensor(t_pts).to(xyz.device).to(xyz.dtype)
+            t_colors = torch.tensor(t_colors).to(rgb.device).to(rgb.dtype)
+            t_scales = torch.tensor(t_scales).to(scales.device).to(scales.dtype)
+            t_quats = torch.tensor(t_quats).to(quat.device).to(quat.dtype)
+            t_opacities = torch.tensor(t_opacities).to(opa.device).to(opa.dtype)
+
+            # add table pos
+            t_pts = t_pts + torch.tensor([mean_xyz[0].item() - 0.36, mean_xyz[1].item() - 0.10, 0.02]).to(t_pts.device).to(t_pts.dtype)
+
+            # add gripper
+            g_pts, g_colors, g_scales, g_quats, g_opacities = self.gripper_params
+            g_pts = torch.tensor(g_pts).to(xyz.device).to(xyz.dtype)
+            g_colors = torch.tensor(g_colors).to(rgb.device).to(rgb.dtype)
+            g_scales = torch.tensor(g_scales).to(scales.device).to(scales.dtype)
+            g_quats = torch.tensor(g_quats).to(quat.device).to(quat.dtype)
+            g_opacities = torch.tensor(g_opacities).to(opa.device).to(opa.dtype)
+
+            g_pts_tip = g_pts[(g_pts[:, 2] > -0.10) & (g_pts[:, 2] < -0.02)]
+            g_pts_tip_mean_xy = g_pts_tip[:, :2].mean(dim=0)
+            g_pts_translation = torch.tensor([-g_pts_tip_mean_xy[0] - 0.02, -g_pts_tip_mean_xy[1] + 0.0, 0.07]).to(g_pts.device).to(g_pts.dtype)
+            g_pts = g_pts + g_pts_translation
+
+            # rotate gripper
+            gripper_mat = kornia.geometry.conversions.quaternion_to_rotation_matrix(gripper_quat)  # (num_steps, num_grippers, 3, 3)
+            g_pts = g_pts @ gripper_mat  # (num_steps, num_grippers, num_points, 3)
+
+            g_quats_mat = kornia.geometry.conversions.quaternion_to_rotation_matrix(g_quats)  # (num_grippers, 3, 3)
+            g_quats_mat = g_quats_mat[None, None].repeat(n_steps, n_grippers, 1, 1, 1)  # (num_steps, num_grippers, num_points, 3, 3)
+            g_quats_mat = gripper_mat.permute(0, 1, 3, 2)[:, :, None] @ g_quats_mat  # (num_steps, num_grippers, num_points, 3, 3)
+            g_quats = kornia.geometry.conversions.rotation_matrix_to_quaternion(g_quats_mat)  # (num_steps, num_grippers, num_points, 4)
+
+            # add gripper pos
+            g_pts = g_pts + gripper_center[:, :, None]
+
+            # reshape
+            g_pts = g_pts.reshape(n_steps, -1, 3)
+            g_colors = g_colors.repeat(n_grippers, 1)
+            g_quats = g_quats.reshape(n_steps, -1, 4)
+            g_opacities = g_opacities.repeat(n_grippers, 1)
+            g_scales = g_scales.repeat(n_grippers, 1)
+
+            # merge
+            bg_xyz = torch.cat([xyz, t_pts[None].repeat(n_steps, 1, 1), g_pts], dim=1)
+            bg_rgb = torch.cat([rgb, t_colors[None].repeat(n_steps, 1, 1), g_colors[None].repeat(n_steps, 1, 1)], dim=1)
+            bg_quat = torch.cat([quat, t_quats[None].repeat(n_steps, 1, 1), g_quats], dim=1)
+            bg_opa = torch.cat([opa, t_opacities[None].repeat(n_steps, 1, 1), g_opacities[None].repeat(n_steps, 1, 1)], dim=1)
+            bg_scales = torch.cat([scales, t_scales[None].repeat(n_steps, 1, 1), g_scales[None].repeat(n_steps, 1, 1)], dim=1)
+
+            bg_quat = torch.nn.functional.normalize(bg_quat, dim=-1)
+
+        rendervar_list = []
+        rendervar_list_bg = []
+        for t in range(n_steps):
+            rendervar = {
+                'means3D': xyz[t],  
+                'colors_precomp': rgb[t],
+                'rotations': quat[t],
+                'opacities': opa[t],
+                'scales': scales[t],
+                'means2D': torch.zeros_like(xyz[t]),
+            }
+            rendervar_list.append(rendervar)
+
+            if with_bg:
+                rendervar_bg = {
+                    'means3D': bg_xyz[t],  
+                    'colors_precomp': bg_rgb[t],
+                    'rotations': bg_quat[t],
+                    'opacities': bg_opa[t],
+                    'scales': bg_scales[t],
+                    'means2D': torch.zeros_like(bg_xyz[t]),
+                }
+                rendervar_list_bg.append(rendervar_bg)
+
+        return rendervar_list, rendervar_list_bg
+
+
+def inverse_preprocess(cfg, p_x, grippers, source_data_root_episode):
+    dx = cfg.sim.num_grids_flexible[-1]
+
+    xyz_orig = np.load(source_data_root_episode / 'traj.npz')['xyz']
+    xyz = torch.tensor(xyz_orig, dtype=torch.float32)
+    
+    R = torch.tensor(
+        [[1, 0, 0],
+        [0, 0, -1],
+        [0, 1, 0]]
+    ).to(xyz.device).to(xyz.dtype)
+    xyz = torch.einsum('nij,jk->nik', xyz, R.T)
+
+    scale = cfg.sim.preprocess_scale
+    xyz = xyz * scale
+    
+    if cfg.sim.preprocess_with_table:
+        global_translation = torch.tensor([
+            0.5 - (xyz[:, :, 0].max() + xyz[:, :, 0].min()) / 2,
+            dx * (cfg.model.clip_bound + 0.5) + 1e-5 - xyz[:, :, 1].min(),
+            0.5 - (xyz[:, :, 2].max() + xyz[:, :, 2].min()) / 2,
+        ], dtype=xyz.dtype)
+    else:
+        global_translation = torch.tensor([
+            0.5 - (xyz[:, :, 0].max() + xyz[:, :, 0].min()) / 2,
+            0.5 - (xyz[:, :, 1].max() + xyz[:, :, 1].min()) / 2,
+            0.5 - (xyz[:, :, 2].max() + xyz[:, :, 2].min()) / 2,
+        ], dtype=xyz.dtype)
+
+    p_x -= global_translation
+    grippers[:, :, :3] -= global_translation
+
+    p_x = p_x / scale
+    grippers[:, :, :3] = grippers[:, :, :3] / scale
+
+    p_x = torch.einsum('nij,jk->nik', p_x, torch.linalg.inv(R).T)
+    grippers[:, :, :3] = torch.einsum('nmi,ik->nmk', grippers[:, :, :3], torch.linalg.inv(R).T)
+
+    gripper_quat = grippers[:, :, 6:10]  # (n_steps, n_grippers, 4)
+    gripper_rot = kornia.geometry.conversions.quaternion_to_rotation_matrix(gripper_quat)  # (n_steps, n_gripper, 3, 3)
+    gripper_rot = R.T @ gripper_rot @ R
+    gripper_quat = kornia.geometry.conversions.rotation_matrix_to_quaternion(gripper_rot)
+    grippers[:, :, 6:10] = gripper_quat
+
+    return p_x, grippers
+
+
+def get_camera(cfg, log_root, source_data_dir, source_episode_id, frame_id=0, camera_id=1):
+    h, w = 480, 848
+    calibration_dir = (log_root.parent.parent / source_data_dir).parent / f'episode_{source_episode_id:04d}' / 'calibration'
+    intr = np.load(calibration_dir / 'intrinsics.npy')
+    rvec = np.load(calibration_dir / 'rvecs.npy')
+    tvec = np.load(calibration_dir / 'tvecs.npy')
+    R = [cv2.Rodrigues(rvec[i])[0] for i in range(rvec.shape[0])]
+    T = [tvec[i, :, 0] for i in range(tvec.shape[0])]
+    extrs = np.zeros((len(R), 4, 4)).astype(np.float32)
+    for i in range(len(R)):
+        extrs[i, :3, :3] = R[i]
+        extrs[i, :3, 3] = T[i]
+        extrs[i, 3, 3] = 1
+    return {
+        'w': w,
+        'h': h,
+        'intr': intr[camera_id],
+        'w2c': extrs[camera_id],
+    }
+
+
+@torch.no_grad()
+def render(
+    cfg,
+    log_root,
+    iteration,
+    episode_names,
+    eval_dirname='eval',
+    eval_postfix='',
+    dataset_name='',
+    camera_id=1,
+    with_bg=False,
+    with_mask=False,
+    transparent=True,
+    start_step=None,
+    end_step=None,
+):
+
+    if dataset_name == '':
+        eval_name = f'{cfg.train.name}/{eval_dirname}/{iteration:06d}'
+    else:
+        eval_name = f'{cfg.train.name}/{eval_dirname}/{dataset_name}/{iteration:06d}'
+    render_type = 'pv_gs'
+    render_type_gs = 'gs'
+
+    exp_root: Path = log_root / eval_name
+    state_root: Path = exp_root / 'state'
+    image_root: Path = exp_root / render_type
+    gs_root: Path = exp_root / render_type_gs
+    mkdir(image_root, overwrite=cfg.overwrite, resume=cfg.resume)
+    mkdir(gs_root, overwrite=cfg.overwrite, resume=cfg.resume)
+
+    if with_mask:
+        render_type_mask = 'mask'
+        episode_mask_root = exp_root / render_type_mask
+        mkdir(episode_mask_root, overwrite=cfg.overwrite, resume=cfg.resume)
+
+    if with_bg:
+        render_type_bg = 'pv_gs_bg'
+        render_type_gs_bg = 'gs_bg'
+        image_root_bg: Path = exp_root / render_type_bg
+        gs_root_bg: Path = exp_root / render_type_gs_bg
+        mkdir(image_root_bg, overwrite=cfg.overwrite, resume=cfg.resume)
+        mkdir(gs_root_bg, overwrite=cfg.overwrite, resume=cfg.resume)
+
+    video_path_list = []
+    for episode_idx, episode in enumerate(episode_names):
+
+        renderer = GSRenderer(cfg.render)
+
+        meta = np.loadtxt(log_root / str(cfg.train.source_dataset_name) / episode / 'meta.txt')
+        with open(log_root / str(cfg.train.source_dataset_name) / 'metadata.json') as f:
+            datadir_list = json.load(f)
+        episode_real_name = int(episode.split('_')[1])
+        datadir = datadir_list[episode_real_name]
+        source_data_dir = datadir['path']
+        source_episode_id = int(meta[0])
+        source_frame_start = int(meta[1]) + int(cfg.sim.n_history) * int(cfg.train.dataset_load_skip_frame) * int(cfg.train.dataset_skip_frame)
+        source_frame_end = int(meta[2])
+        episode_gs_init_path = (log_root.parent.parent / source_data_dir).parent / f'episode_{source_episode_id:04d}' / 'gs' / f'{source_frame_start:06d}.splat'
+
+        renderer.load_params(episode_gs_init_path)
+
+        episode_state_root = state_root / episode
+        episode_image_root = image_root / episode
+        episode_gs_root = gs_root / episode
+        mkdir(episode_image_root, overwrite=cfg.overwrite, resume=cfg.resume)
+        mkdir(episode_gs_root, overwrite=cfg.overwrite, resume=cfg.resume)
+
+        if with_mask:
+            episode_mask_root = episode_mask_root / episode
+            mkdir(episode_mask_root, overwrite=cfg.overwrite, resume=cfg.resume)
+
+        if with_bg:
+            episode_image_root_bg = image_root_bg / episode
+            episode_gs_root_bg = gs_root_bg / episode
+            mkdir(episode_image_root_bg, overwrite=cfg.overwrite, resume=cfg.resume)
+            mkdir(episode_gs_root_bg, overwrite=cfg.overwrite, resume=cfg.resume)
+
+        ckpt_paths = list(sorted(episode_state_root.glob('*.pt'), key=lambda x: int(x.stem)))
+
+        p_x_list = []
+        grippers_list = []
+        for i, path in enumerate(ckpt_paths):
+            if i % cfg.render.skip_frame != 0:
+                continue
+
+            ckpt = torch.load(path, map_location='cpu')
+            p_x = ckpt['x']
+            p_x_list.append(p_x)
+
+            use_grippers = 'grippers' in ckpt
+            grippers = None
+            if use_grippers:
+                grippers = ckpt['grippers']
+            if grippers is not None:
+                grippers_list.append(grippers)
+        
+        p_x_list = torch.stack(p_x_list, dim=0)
+        grippers_list = torch.stack(grippers_list, dim=0) if grippers_list else []
+        p_x_list, grippers_list = inverse_preprocess(cfg, p_x_list, grippers_list,
+                source_data_root_episode=log_root / cfg.train.source_dataset_name / episode)
+
+        rendervar_list, rendervar_list_bg = renderer.rollout_and_render(p_x_list, grippers_list, with_bg=with_bg)
+
+        for i, path in enumerate(tqdm(ckpt_paths, desc=render_type)):
+            rendervar = rendervar_list[i // cfg.render.skip_frame]
+            renderer.set_camera(**get_camera(cfg, log_root, source_data_dir, source_episode_id, frame_id=i, camera_id=camera_id))
+            im, _ = renderer.render(rendervar, 0)
+            im = im.cpu().numpy().transpose(1, 2, 0)
+            im = (im * 255).astype(np.uint8)
+            im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
+
+            if transparent or with_mask:
+                rendervar['colors_precomp'] = torch.ones_like(rendervar['colors_precomp'])
+                mask, _ = renderer.render(rendervar, 0)
+                mask = mask.cpu().numpy().transpose(1, 2, 0)
+
+                if transparent:
+                    im = cv2.cvtColor(im, cv2.COLOR_RGB2RGBA)
+                    im[:, :, 3] = (mask * 255).mean(-1).astype(np.uint8)
+
+                if with_mask:
+                    thresh = 0.1
+                    mask = (mask > thresh).astype(np.float32)
+                    mask = (mask * 255).astype(np.uint8)
+                    mask = cv2.cvtColor(mask, cv2.COLOR_BGR2GRAY)
+                    save_path = str(episode_mask_root / f'{i // cfg.render.skip_frame:04d}.png')
+                    cv2.imwrite(save_path, mask)
+
+            save_path = str(episode_image_root / f'{i // cfg.render.skip_frame:04d}.png')
+            cv2.imwrite(save_path, im)
+
+            gs_save_path = str(episode_gs_root / f'{i // cfg.render.skip_frame:04d}.splat')
+            save_to_splat(
+                pts=rendervar['means3D'].cpu().numpy(),
+                colors=rendervar['colors_precomp'].cpu().numpy(),
+                scales=rendervar['scales'].cpu().numpy(),
+                quats=rendervar['rotations'].cpu().numpy(),
+                opacities=rendervar['opacities'].cpu().numpy(),
+                output_file=gs_save_path,
+                center=False,
+                rotate=False,
+            )
+
+            if with_bg:
+                rendervar_bg = rendervar_list_bg[i // cfg.render.skip_frame]
+                renderer.set_camera(**get_camera(cfg, log_root, source_data_dir, source_episode_id, frame_id=i, camera_id=camera_id))
+                im, _ = renderer.render(rendervar_bg, 0)
+                im = im.cpu().numpy().transpose(1, 2, 0)
+                im = (im * 255).astype(np.uint8)
+                im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
+                
+                if transparent:
+                    rendervar_bg['colors_precomp'] = torch.ones_like(rendervar_bg['colors_precomp'])
+                    mask, _ = renderer.render(rendervar_bg, 0)
+                    mask = mask.cpu().numpy().transpose(1, 2, 0)
+                    im = cv2.cvtColor(im, cv2.COLOR_RGB2RGBA)
+                    im[:, :, 3] = (mask * 255).mean(-1).astype(np.uint8)
+
+                save_path = str(episode_image_root_bg / f'{i // cfg.render.skip_frame:04d}.png')
+                cv2.imwrite(save_path, im)
+
+                gs_save_path = str(episode_gs_root_bg / f'{i // cfg.render.skip_frame:04d}.splat')
+                save_to_splat(
+                    pts=rendervar_bg['means3D'].cpu().numpy(),
+                    colors=rendervar_bg['colors_precomp'].cpu().numpy(),
+                    scales=rendervar_bg['scales'].cpu().numpy(),
+                    quats=rendervar_bg['rotations'].cpu().numpy(),
+                    opacities=rendervar_bg['opacities'].cpu().numpy(),
+                    output_file=gs_save_path,
+                    center=False,
+                    rotate=False,
+                )
+
+        make_video(episode_image_root, image_root / f'{episode}{eval_postfix}.mp4', '%04d.png', cfg.render.fps)
+        video_path_list.append(image_root / f'{episode}{eval_postfix}.mp4')
+    
+        if with_bg:
+            make_video(episode_image_root_bg, image_root_bg / f'{episode}{eval_postfix}.mp4', '%04d.png', cfg.render.fps)
+    
+    return video_path_list
+
+
+@torch.no_grad()
+def do_gs(*args, **kwargs):
+    ret = render(*args, **kwargs)
+    return ret
diff --git a/src/experiments/train/metric.py b/src/experiments/train/metric.py
new file mode 100644
index 0000000000000000000000000000000000000000..7b14ffe647f4f594be9d9df84b88718d46f20313
--- /dev/null
+++ b/src/experiments/train/metric.py
@@ -0,0 +1,256 @@
+from pathlib import Path
+import random
+import time
+from omegaconf import DictConfig, OmegaConf
+import numpy as np
+import torch
+import os
+import glob
+from PIL import Image
+import argparse
+import yaml
+import scipy
+import math
+import lpips
+import cv2
+import matplotlib.pyplot as plt
+from skimage.metrics import structural_similarity as ssim_sk
+
+from pgnd.utils import get_root
+
+root: Path = get_root(__file__)
+
+def calc_psnr(img1, img2, mask):
+    # img1: (H, W, 3)
+    # img2: (H, W, 3)
+    # mask: (H, W)
+    mse = np.sum((img1 - img2) ** 2 * mask[..., None]) / np.sum(mask)
+    if mse == 0:
+        return 100
+    PIXEL_MAX = 255.0
+    return 20 * np.log10(PIXEL_MAX / np.sqrt(mse))
+
+
+def calc_ssim(img1, img2, mask):
+    # img1: (H, W, 3)
+    # img2: (H, W, 3)
+    # mask: (H, W)
+    return ssim_sk(img1, img2, data_range=255, mask=mask, multichannel=True, channel_axis=2)
+
+
+def mse_dist(xyz, xyz_gt):
+    # xyz: (N, 3)
+    # xyz_gt: (N, 3)
+    return torch.mean(torch.norm(xyz - xyz_gt, 2, dim=1)).item()
+
+
+def chamfer_dist(xyz, xyz_gt):
+    # xyz: (N, 3)
+    # xyz_gt: (N, 3)
+    dist1 = torch.sqrt(torch.sum((xyz[:, None] - xyz_gt[None]) ** 2, dim=2))
+    dist2 = torch.sqrt(torch.sum((xyz_gt[:, None] - xyz[None]) ** 2, dim=2))
+    chamfer = torch.mean(torch.min(dist1, dim=1).values) + torch.mean(torch.min(dist2, dim=1).values)
+    return chamfer.item()
+
+def em_distance(x, y):
+    # x: [N, D]
+    # y: [M, D]
+    cost_matrix = scipy.spatial.distance.cdist(x.cpu(), y.cpu())
+    try:
+        ind1, ind2 = scipy.optimize.linear_sum_assignment(
+            cost_matrix, maximize=False
+        )
+    except:
+        print("Error in linear sum assignment!")
+    ind1 = torch.tensor(ind1).to(x.device)
+    ind2 = torch.tensor(ind2).to(y.device)
+    x_new = x[ind1]
+    y_new = y[ind2]
+
+    emd = torch.mean(torch.norm(x_new - y_new, 2, dim=1))
+    return emd.item()
+
+
+def seg2bmap(seg, width=None, height=None):
+    """
+    From a segmentation, compute a binary boundary map with 1 pixel wide
+    boundaries.  The boundary pixels are offset by 1/2 pixel towards the
+    origin from the actual segment boundary.
+    Arguments:
+        seg     : Segments labeled from 1..k.
+        width	  :	Width of desired bmap  <= seg.shape[1]
+        height  :	Height of desired bmap <= seg.shape[0]
+    Returns:
+        bmap (ndarray):	Binary boundary map.
+        David Martin <dmartin@eecs.berkeley.edu>
+        January 2003
+    """
+
+    seg = seg.astype(bool)
+    seg[seg > 0] = 1
+
+    assert np.atleast_3d(seg).shape[2] == 1
+
+    width = seg.shape[1] if width is None else width
+    height = seg.shape[0] if height is None else height
+
+    h, w = seg.shape[:2]
+
+    ar1 = float(width) / float(height)
+    ar2 = float(w) / float(h)
+
+    assert not (
+            width > w | height > h | abs(ar1 - ar2) > 0.01
+    ), "Can" "t convert %dx%d seg to %dx%d bmap." % (w, h, width, height)
+
+    e = np.zeros_like(seg)
+    s = np.zeros_like(seg)
+    se = np.zeros_like(seg)
+
+    e[:, :-1] = seg[:, 1:]
+    s[:-1, :] = seg[1:, :]
+    se[:-1, :-1] = seg[1:, 1:]
+
+    b = seg ^ e | seg ^ s | seg ^ se
+    b[-1, :] = seg[-1, :] ^ e[-1, :]
+    b[:, -1] = seg[:, -1] ^ s[:, -1]
+    b[-1, -1] = 0
+
+    if w == width and h == height:
+        bmap = b
+    else:
+        bmap = np.zeros((height, width))
+        for x in range(w):
+            for y in range(h):
+                if b[y, x]:
+                    j = 1 + math.floor((y - 1) + height / h)
+                    i = 1 + math.floor((x - 1) + width / h)
+                    bmap[j, i] = 1
+
+    return bmap
+
+def compute_f(mask, mask_gt):
+    # Only loaded when run to reduce minimum requirements
+    # from pycocotools import mask as mask_utils
+    from skimage.morphology import disk
+    import cv2
+
+    bound_th = 0.008
+    
+    bound_pix = bound_th if bound_th >= 1 - np.finfo('float').eps else \
+        np.ceil(bound_th * np.linalg.norm(mask.shape))
+
+    # Get the pixel boundaries of both masks
+    fg_boundary = seg2bmap(mask)
+    gt_boundary = seg2bmap(mask_gt)
+
+    # fg_dil = binary_dilation(fg_boundary, disk(bound_pix))
+    fg_dil = cv2.dilate(fg_boundary.astype(np.uint8), disk(bound_pix).astype(np.uint8))
+    # gt_dil = binary_dilation(gt_boundary, disk(bound_pix))
+    gt_dil = cv2.dilate(gt_boundary.astype(np.uint8), disk(bound_pix).astype(np.uint8))
+
+    # Get the intersection
+    gt_match = gt_boundary * fg_dil
+    fg_match = fg_boundary * gt_dil
+
+    # Area of the intersection
+    n_fg = np.sum(fg_boundary)
+    n_gt = np.sum(gt_boundary)
+
+    # % Compute precision and recall
+    if n_fg == 0 and n_gt > 0:
+        precision = 1
+        recall = 0
+    elif n_fg > 0 and n_gt == 0:
+        precision = 0
+        recall = 1
+    elif n_fg == 0 and n_gt == 0:
+        precision = 1
+        recall = 1
+    else:
+        precision = np.sum(fg_match) / float(n_fg)
+        recall = np.sum(gt_match) / float(n_gt)
+
+    # Compute F measure
+    if precision + recall == 0:
+        f_val = 0
+    else:
+        f_val = 2 * precision * recall / (precision + recall)
+
+    return f_val
+
+def compute_j(mask, mask_gt):
+    iou = np.sum(mask & mask_gt) / np.sum(mask | mask_gt)
+    return iou
+
+
+def compute_lpips(fn, im, im_gt):
+    im = torch.tensor(im).permute(2, 0, 1).unsqueeze(0).float()
+    im_gt = torch.tensor(im_gt).permute(2, 0, 1).unsqueeze(0).float()
+    im = im / 255.0
+    im_gt = im_gt / 255.0
+    perception = fn.forward(im, im_gt)
+    return perception.item()
+
+
+def inverse_preprocess(cfg, p_x, xyz):
+    dx = cfg.sim.num_grids[-1]
+
+    R = torch.tensor(
+        [[1, 0, 0],
+        [0, 0, -1],
+        [0, 1, 0]]
+    ).to(xyz.device).to(xyz.dtype)
+    xyz = torch.einsum('nij,jk->nik', xyz, R.T)
+
+    scale = cfg.sim.preprocess_scale
+    xyz = xyz * scale
+    
+    if cfg.sim.preprocess_with_table:
+        global_translation = torch.tensor([
+            0.5 - (xyz[:, :, 0].max() + xyz[:, :, 0].min()) / 2,
+            dx * (cfg.model.clip_bound + 0.5) + 1e-5 - xyz[:, :, 1].min(),
+            0.5 - (xyz[:, :, 2].max() + xyz[:, :, 2].min()) / 2,
+        ], dtype=xyz.dtype)
+    else:
+        global_translation = torch.tensor([
+            0.5 - (xyz[:, :, 0].max() + xyz[:, :, 0].min()) / 2,
+            0.5 - (xyz[:, :, 1].max() + xyz[:, :, 1].min()) / 2,
+            0.5 - (xyz[:, :, 2].max() + xyz[:, :, 2].min()) / 2,
+        ], dtype=xyz.dtype)
+    
+    xyz += global_translation
+    if not (xyz[:, :, 0].min() >= dx * (cfg.model.clip_bound + 0.5) - 1e-6 \
+        and xyz[:, :, 0].max() <= 1 - dx * (cfg.model.clip_bound + 0.5) + 1e-6 \
+        and xyz[:, :, 1].min() >= dx * (cfg.model.clip_bound + 0.5) - 1e-6 \
+        and xyz[:, :, 1].max() <= 1 - dx * (cfg.model.clip_bound + 0.5) + 1e-6 \
+        and xyz[:, :, 2].min() >= dx * (cfg.model.clip_bound + 0.5) - 1e-6 \
+        and xyz[:, :, 2].max() <= 1 - dx * (cfg.model.clip_bound + 0.5) + 1e-6):
+        print('inverse_preprocess out of bound')
+        xyz_max = xyz.max(dim=0).values
+        xyz_max_mask = (xyz_max[:, 0] > 1 - dx * (cfg.model.clip_bound + 0.5) + 1e-6) | \
+            (xyz_max[:, 1] > 1 - dx * (cfg.model.clip_bound + 0.5) + 1e-6) | \
+            (xyz_max[:, 2] > 1 - dx * (cfg.model.clip_bound + 0.5) + 1e-6)
+        xyz_min = xyz.min(dim=0).values
+        xyz_min_mask = (xyz_min[:, 0] < dx * (cfg.model.clip_bound + 0.5) - 1e-6) | \
+            (xyz_min[:, 1] < dx * (cfg.model.clip_bound + 0.5) - 1e-6) | \
+            (xyz_min[:, 2] < dx * (cfg.model.clip_bound + 0.5) - 1e-6)
+        xyz_mask = xyz_max_mask | xyz_min_mask
+        xyz = xyz[:, ~xyz_mask]
+        
+        assert (xyz[:, :, 0].min() >= dx * (cfg.model.clip_bound + 0.5) - 1e-6 \
+            and xyz[:, :, 0].max() <= 1 - dx * (cfg.model.clip_bound + 0.5) + 1e-6 \
+            and xyz[:, :, 1].min() >= dx * (cfg.model.clip_bound + 0.5) - 1e-6 \
+            and xyz[:, :, 1].max() <= 1 - dx * (cfg.model.clip_bound + 0.5) + 1e-6 \
+            and xyz[:, :, 2].min() >= dx * (cfg.model.clip_bound + 0.5) - 1e-6 \
+            and xyz[:, :, 2].max() <= 1 - dx * (cfg.model.clip_bound + 0.5) + 1e-6)
+    xyz -= global_translation
+
+    p_x -= global_translation
+    p_x = p_x / scale
+    p_x = torch.einsum('nij,jk->nik', p_x, torch.linalg.inv(R).T)
+
+    # optional: recover xyz
+    xyz = xyz / scale
+    xyz = torch.einsum('nij,jk->nik', xyz, torch.linalg.inv(R).T)
+    return p_x, xyz
diff --git a/src/experiments/train/metric_eval.py b/src/experiments/train/metric_eval.py
new file mode 100644
index 0000000000000000000000000000000000000000..cfad87075d1ceac94e73ad33045857b22536646c
--- /dev/null
+++ b/src/experiments/train/metric_eval.py
@@ -0,0 +1,198 @@
+from pathlib import Path
+import random
+import time
+from omegaconf import DictConfig, OmegaConf
+
+import numpy as np
+import torch
+import os
+import glob
+from PIL import Image
+import argparse
+import yaml
+import scipy
+import math
+import lpips
+import cv2
+from skimage.metrics import structural_similarity as ssim_sk
+import json
+
+from pgnd.utils import get_root
+from pgnd.ffmpeg import make_video
+from train.metric import mse_dist, chamfer_dist, em_distance, compute_j, compute_f, compute_lpips, calc_psnr, calc_ssim, inverse_preprocess
+
+root: Path = get_root(__file__)
+
+@torch.no_grad()
+def do_metric(
+    cfg,
+    log_root,
+    iteration,
+    episode_names,
+    downsample_indices,
+    eval_dirname,
+    camera_id=1,
+    eval_postfix='',
+    dataset_name='',
+    use_gs=True,
+    eval_camera_num=0,
+):
+
+    state_dir = log_root / cfg.train.name / eval_dirname / dataset_name / f'{iteration:06d}' / 'state'
+
+    if use_gs:
+        pv_gs_dir = log_root / cfg.train.name / eval_dirname / dataset_name / f'{iteration:06d}' / 'pv_gs'
+        mask_dir = log_root / cfg.train.name / eval_dirname / dataset_name / f'{iteration:06d}' / 'mask'
+        
+        pv_gs_gt_dir = log_root / cfg.train.name / eval_dirname / dataset_name / f'{iteration:06d}' / 'pv_gs_gt'
+        mask_gt_dir = log_root / cfg.train.name / eval_dirname / dataset_name / f'{iteration:06d}' / 'mask_gt'
+
+    save_dir = log_root / cfg.train.name / eval_dirname / dataset_name / f'{iteration:06d}' / 'metric'
+    save_dir.mkdir(parents=True, exist_ok=True)
+
+    loss_fn_vgg = lpips.LPIPS(net='alex')
+
+    metric_list_list = []
+
+    for episode_idx, episode in enumerate(episode_names):
+        state_dir_episode = state_dir / episode
+        save_dir_episode = save_dir / episode
+        save_dir_episode.mkdir(parents=True, exist_ok=True)
+
+        if use_gs:
+            pv_gs_dir_episode = pv_gs_dir / episode
+            mask_dir_episode = mask_dir / episode
+            pv_gs_gt_dir_episode = pv_gs_gt_dir / episode
+            mask_gt_dir_episode = mask_gt_dir / episode
+            pv_gs_gt_dir_episode.mkdir(parents=True, exist_ok=True)
+            mask_gt_dir_episode.mkdir(parents=True, exist_ok=True)
+
+        # save downsample_indices
+        downsample_indices = downsample_indices[0].cuda()
+        np.save(save_dir_episode / 'downsample_indices.npy', downsample_indices.cpu().numpy())
+
+        source_dataset_root = log_root / str(cfg.train.source_dataset_name)
+
+        meta = np.loadtxt(log_root / str(cfg.train.source_dataset_name) / episode / 'meta.txt')
+        with open(log_root / str(cfg.train.source_dataset_name) / 'metadata.json') as f:
+            datadir_list = json.load(f)
+        episode_real_name = int(episode.split('_')[1])
+        datadir = datadir_list[episode_real_name]
+        source_data_dir = datadir['path']
+        source_episode_id = int(meta[0])
+        source_frame_start = int(meta[1])
+        source_frame_end = int(meta[2])
+
+        skip_frame = cfg.train.dataset_load_skip_frame * cfg.train.dataset_skip_frame
+        frame_ids = np.arange(source_frame_start + (cfg.sim.n_history + 1) * skip_frame, source_frame_end, skip_frame)
+        n_frames = len(frame_ids)
+
+        # load xyz_orig for inverse preprocess
+        xyz_orig = np.load(source_dataset_root / episode / 'traj.npz')['xyz']
+        xyz_orig = torch.tensor(xyz_orig, dtype=torch.float32)
+
+        traj = []
+        if use_gs:
+            imgs = []
+            masks = []
+            gt_imgs = []
+            gt_masks = []
+
+        data_init = torch.load(log_root / cfg.train.dataset_name / 'state' / episode / f'{0:04d}.pt')
+
+        for frame_id in range(n_frames):
+            frame_id_gt = frame_ids[frame_id]
+
+            state = torch.load(state_dir_episode / f'{frame_id:04d}.pt')
+            x = state['x'].cpu()
+            traj.append(x)  # (n, 3)
+
+            if use_gs:
+                pv_gs = cv2.imread(pv_gs_dir_episode / f'{frame_id:04d}.png')
+                pv_gs = cv2.cvtColor(pv_gs, cv2.COLOR_BGR2RGB)
+                mask = cv2.imread(mask_dir_episode / f'{frame_id:04d}.png')
+                mask = cv2.cvtColor(mask, cv2.COLOR_BGR2GRAY)
+
+                imgs.append(pv_gs)  # (H, W, 3)
+                masks.append(mask)  # (H, W, 3)
+
+                gt_mask = cv2.imread((log_root.parent.parent / source_data_dir).parent / f'episode_{source_episode_id:04d}' \
+                        / f'camera_{camera_id}' / 'mask' / f'{int(frame_id_gt):06d}.png')
+                gt_mask = cv2.cvtColor(gt_mask, cv2.COLOR_BGR2GRAY)
+                gt_masks.append(gt_mask)
+
+                gt_img = cv2.imread((log_root.parent.parent / source_data_dir).parent / f'episode_{source_episode_id:04d}' \
+                        / f'camera_{camera_id}' / 'rgb' / f'{int(frame_id_gt):06d}.jpg')
+                gt_img = cv2.cvtColor(gt_img, cv2.COLOR_BGR2RGB)
+                gt_img = gt_img * (gt_mask > 0)[..., None]
+                gt_imgs.append(gt_img)
+
+                # save
+                pv_gs_gt = Image.fromarray(gt_img)
+                pv_gs_gt.save(pv_gs_gt_dir_episode / f'{frame_id:04d}.png')
+                mask_gt = Image.fromarray(gt_mask)
+                mask_gt.save(mask_gt_dir_episode / f'{frame_id:04d}.png')
+        
+        if use_gs:
+            frame_rate = 10
+            video_name = pv_gs_gt_dir / f'{episode}.mp4'
+            make_video(pv_gs_gt_dir_episode, video_name, '%04d.png', frame_rate)
+        
+        traj = torch.stack(traj, dim=0)
+        traj, xyz_orig = inverse_preprocess(cfg, traj, xyz_orig)
+        gt_traj = xyz_orig[(cfg.sim.n_history + 1) * skip_frame::skip_frame]
+
+        if use_gs:
+            assert len(imgs) == len(gt_imgs) == len(gt_masks) == len(traj) == len(gt_traj)
+        else:
+            assert len(traj) == len(gt_traj)
+
+        metric_list = []
+        for i in range(len(traj)):
+            xyz = traj[i].cuda()
+            xyz_gt = gt_traj[i].cuda()
+
+            if use_gs:
+                im = imgs[i]
+                mask = masks[i]
+                im_gt = gt_imgs[i]
+                mask_gt = gt_masks[i]
+
+                mask = mask > 0
+                mask_gt = mask_gt > 0
+
+            xyz_gt_downsampled = xyz_gt[downsample_indices]
+
+            mse = mse_dist(xyz, xyz_gt_downsampled)
+            chamfer = chamfer_dist(xyz, xyz_gt)
+            emd = em_distance(xyz, xyz_gt)
+
+            if use_gs:
+                jscore = compute_j(mask, mask_gt)
+                fscore = compute_f(mask, mask_gt)
+                jfscore = (jscore + fscore) / 2
+
+                perception = compute_lpips(loss_fn_vgg, im, im_gt)
+                psnr = calc_psnr(im, im_gt, mask_gt)
+                ssim = calc_ssim(im, im_gt, mask_gt)
+                iou = np.sum(mask & mask_gt) / np.sum(mask | mask_gt)
+
+                metric_list.append([mse, chamfer, emd, jscore, fscore, jfscore, perception, psnr, ssim, iou])
+                print(f'{episode}, image: {i}, camera: {camera_id}', end=' ')
+                print(f'3D MSE: {mse:.4f}, 3D CD: {chamfer:.4f}, 3D EMD: {emd:.4f}', end=' ')
+                print(f'J-Score: {jscore:.4f}, F-Score: {fscore:.4f}, JF-Score: {jfscore:.4f}', end=' ')
+                print(f'perception: {perception:.4f}, PSNR: {psnr:.4f}, SSIM: {ssim:.4f}, IoU: {iou:.4f}')
+
+            else:
+                metric_list.append([mse, chamfer, emd])
+                print(f'{episode}, image: {i}', end=' ')
+                print(f'3D MSE: {mse:.4f}, 3D CD: {chamfer:.4f}, 3D EMD: {emd:.4f}')
+
+        # save metrics
+        metric_list = np.array(metric_list)
+        np.savetxt(save_dir_episode / f'metric.txt', metric_list, fmt='%.6f')
+
+        metric_list_list.append(metric_list)
+
+    metric_list_list = np.array(metric_list_list)
+    return metric_list_list
diff --git a/src/experiments/train/pv_dataset.py b/src/experiments/train/pv_dataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..77a734f1248f765b6bd62765457bb67ad4d1da4c
--- /dev/null
+++ b/src/experiments/train/pv_dataset.py
@@ -0,0 +1,120 @@
+from pathlib import Path
+import random
+from tqdm import tqdm, trange
+
+import hydra
+from omegaconf import DictConfig, OmegaConf
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.backends.cudnn
+import math
+import pyvista as pv
+from dgl.geometry import farthest_point_sampler
+
+from pgnd.utils import get_root, mkdir
+from pgnd.ffmpeg import make_video
+
+from train.pv_utils import Xvfb, get_camera_custom
+
+
+def fps(x, n, random_start=False):
+    start_idx = random.randint(0, x.shape[0] - 1) if random_start else 0
+    fps_idx = farthest_point_sampler(x[None], n, start_idx=start_idx)[0]
+    fps_idx = fps_idx.to(x.device)
+    return fps_idx
+
+
+@torch.no_grad()
+def render(
+    cfg,
+    dataset_root,
+    episode_names,
+    iteration=None,
+    start_step=None,
+    end_step=None,
+    save_dir=None,
+    downsample_indices=None,
+    clean_bg=False,
+):
+    render_type = 'pv'
+
+    exp_root: Path = dataset_root
+    state_root: Path = exp_root / 'state'
+
+    video_path_list = []
+    for episode_idx, episode in enumerate(episode_names):
+
+        plotter = pv.Plotter(lighting='three lights', off_screen=True, window_size=(cfg.render.width, cfg.render.height))
+        plotter.set_background('white')
+        plotter.camera_position = get_camera_custom(cfg.render.center, cfg.render.distance, cfg.render.azimuth, cfg.render.elevation)
+        plotter.enable_shadows()
+
+        # add bounding box
+        scale_x = cfg.sim.num_grids[0] / (cfg.sim.num_grids[0] - 2 * cfg.render.bound)
+        scale_y = cfg.sim.num_grids[1] / (cfg.sim.num_grids[1] - 2 * cfg.render.bound)
+        scale_z = cfg.sim.num_grids[2] / (cfg.sim.num_grids[2] - 2 * cfg.render.bound)
+        scale = np.array([scale_x, scale_y, scale_z])
+        scale_mean = np.power(np.prod(scale), 1 / 3)
+        bbox = pv.Box(bounds=[0, 1, 0, 1, 0, 1])
+        if not clean_bg:
+            plotter.add_mesh(bbox, style='wireframe', color='black')
+        
+        # add axis
+        if not clean_bg:
+            for axis, color in enumerate(['r', 'g', 'b']):
+                mesh = pv.Arrow(start=[0, 0, 0], direction=np.eye(3)[axis], scale=0.2)
+                plotter.add_mesh(mesh, color=color, show_scalar_bar=False)
+
+        episode_state_root = state_root / episode
+
+        episode_image_root = save_dir / f'{episode}_gt'
+        mkdir(episode_image_root, overwrite=True, resume=True)
+
+        ckpt_paths = list(sorted(episode_state_root.glob('*.pt'), key=lambda x: int(x.stem)))
+        if start_step is not None and end_step is not None:
+            ckpt_paths = ckpt_paths[start_step:end_step]
+        skip_frame = cfg.train.dataset_skip_frame * cfg.train.dataset_load_skip_frame
+        ckpt_paths = ckpt_paths[skip_frame::skip_frame]
+        for i, path in enumerate(tqdm(ckpt_paths, desc=render_type)):
+
+            if i % cfg.render.skip_frame != 0:
+                continue
+
+            ckpt = torch.load(path, map_location='cpu')
+            p_x = ckpt['x'].cpu().detach().numpy()
+            if downsample_indices is not None:
+                p_x = p_x[downsample_indices[0]]
+            else:
+                downsample_indices = fps(torch.from_numpy(p_x), cfg.sim.n_particles, random_start=True)[None]
+                p_x = p_x[downsample_indices[0]]
+
+            x = (p_x - 0.5) * scale + 0.5
+
+            grippers = ckpt['grippers'].cpu().detach().numpy()
+            n_eef = grippers.shape[0]
+
+            radius = 0.5 * np.power((0.5 ** 3) / x.shape[0], 1 / 3) * scale_mean
+            x = np.clip(x, radius, 1 - radius)
+
+            polydata = pv.PolyData(x)
+            plotter.add_mesh(polydata, style='points', name='object', render_points_as_spheres=True, point_size=radius * cfg.render.radius_scale, color=list(cfg.render.reflectance))
+            for j in range(n_eef):
+                gripper = pv.Sphere(center=grippers[j, :3], radius=grippers[j, -2])
+                plotter.add_mesh(gripper, color='blue', name=f'gripper_{j}')
+
+            plotter.show(auto_close=False, screenshot=str(episode_image_root / f'{i // cfg.render.skip_frame:04d}.png'))
+
+        plotter.close()
+        if save_dir is not None:
+            make_video(episode_image_root, save_dir / f'{episode}_gt.mp4', '%04d.png', cfg.render.fps)
+            video_path_list.append(save_dir / f'{episode}_gt.mp4')
+
+    return video_path_list
+
+
+@torch.no_grad()
+def do_dataset_pv(*args, **kwargs):
+    with Xvfb():
+        ret = render(*args, **kwargs)
+    return ret
\ No newline at end of file
diff --git a/src/experiments/train/pv_train.py b/src/experiments/train/pv_train.py
new file mode 100644
index 0000000000000000000000000000000000000000..b962255ac4be4bb5d9b990dfe509f2fd2b935ce2
--- /dev/null
+++ b/src/experiments/train/pv_train.py
@@ -0,0 +1,117 @@
+from pathlib import Path
+import random
+from tqdm import tqdm, trange
+
+import hydra
+from omegaconf import DictConfig, OmegaConf
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.backends.cudnn
+import math
+import pyvista as pv
+
+from pgnd.utils import get_root, mkdir
+from pgnd.ffmpeg import make_video
+
+from train.pv_utils import Xvfb, get_camera_custom
+
+
+@torch.no_grad()
+def render(
+    cfg,
+    log_root,
+    iteration,
+    episode_names,
+    eval_dirname='eval',
+    eval_postfix='',
+    dataset_name='',
+    start_step=None,
+    end_step=None,
+    clean_bg=False,
+):
+    clean_bg = False
+
+    if dataset_name == '':
+        eval_name = f'{cfg.train.name}/{eval_dirname}/{iteration:06d}'
+    else:
+        eval_name = f'{cfg.train.name}/{eval_dirname}/{dataset_name}/{iteration:06d}'
+    render_type = 'pv'
+
+    exp_root: Path = log_root / eval_name
+    state_root: Path = exp_root / 'state'
+    image_root: Path = exp_root / render_type
+    mkdir(image_root, overwrite=cfg.overwrite, resume=cfg.resume)
+
+
+    video_path_list = []
+    for episode_idx, episode in enumerate(episode_names):
+
+        plotter = pv.Plotter(lighting='three lights', off_screen=True, window_size=(cfg.render.width, cfg.render.height))
+        plotter.set_background('white')
+        plotter.camera_position = get_camera_custom(cfg.render.center, cfg.render.distance, cfg.render.azimuth, cfg.render.elevation)
+        plotter.enable_shadows()
+
+        # add bounding box
+        scale_x = cfg.sim.num_grids[0] / (cfg.sim.num_grids[0] - 2 * cfg.render.bound)
+        scale_y = cfg.sim.num_grids[1] / (cfg.sim.num_grids[1] - 2 * cfg.render.bound)
+        scale_z = cfg.sim.num_grids[2] / (cfg.sim.num_grids[2] - 2 * cfg.render.bound)
+        scale = np.array([scale_x, scale_y, scale_z])
+        scale_mean = np.power(np.prod(scale), 1 / 3)
+        bbox = pv.Box(bounds=[0, 1, 0, 1, 0, 1])
+        if not clean_bg:
+            plotter.add_mesh(bbox, style='wireframe', color='black')
+        
+        # add axis
+        if not clean_bg:
+            for axis, color in enumerate(['r', 'g', 'b']):
+                mesh = pv.Arrow(start=[0, 0, 0], direction=np.eye(3)[axis], scale=0.2)
+                plotter.add_mesh(mesh, color=color, show_scalar_bar=False)
+
+        episode_state_root = state_root / episode
+        episode_image_root = image_root / episode
+        mkdir(episode_image_root, overwrite=cfg.overwrite, resume=cfg.resume)
+
+        ckpt_paths = list(sorted(episode_state_root.glob('*.pt'), key=lambda x: int(x.stem)))
+        for i, path in enumerate(tqdm(ckpt_paths, desc=render_type)):
+
+            if i % cfg.render.skip_frame != 0:
+                continue
+
+            ckpt = torch.load(path, map_location='cpu')
+            p_x = ckpt['x'].cpu().detach().numpy()
+
+            x = (p_x - 0.5) * scale + 0.5
+
+            grippers = ckpt['grippers'].cpu().detach().numpy()
+            n_eef = grippers.shape[0]
+
+            radius = 0.5 * np.power((0.5 ** 3) / x.shape[0], 1 / 3) * scale_mean
+            x = np.clip(x, radius, 1 - radius)
+
+            polydata = pv.PolyData(x)
+            plotter.add_mesh(polydata, style='points', name='object', render_points_as_spheres=True, point_size=radius * cfg.render.radius_scale, color=list(cfg.render.reflectance))
+            for j in range(n_eef):
+                gripper = pv.Sphere(center=grippers[j, :3], radius=0.04)
+                plotter.add_mesh(gripper, color='blue', name=f'gripper_{j}')
+            if 'gripper_x' in ckpt:
+                gripper_points = ckpt['gripper_x'].cpu().detach().numpy()
+                gripper_points = (gripper_points - 0.5) * scale + 0.5
+                gripper_points = np.clip(gripper_points, radius, 1 - radius)
+                gripper_polydata = pv.PolyData(gripper_points)
+                plotter.add_mesh(gripper_polydata, style='points', name=f'gripper_points', render_points_as_spheres=True, point_size=radius * cfg.render.radius_scale, color='blue')
+
+            plotter.show(auto_close=False, screenshot=str(episode_image_root / f'{i // cfg.render.skip_frame:04d}.png'))
+
+        plotter.close()
+        make_video(episode_image_root, image_root / f'{episode}{eval_postfix}.mp4', '%04d.png', cfg.render.fps)
+        video_path_list.append(image_root / f'{episode}{eval_postfix}.mp4')
+
+    return video_path_list
+
+
+@torch.no_grad()
+def do_train_pv(*args, **kwargs):
+    with Xvfb():
+        ret = render(*args, **kwargs)
+    return ret
diff --git a/src/experiments/train/pv_utils.py b/src/experiments/train/pv_utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..a3a62bfd507b16844f834d1047acb9fc0012b6aa
--- /dev/null
+++ b/src/experiments/train/pv_utils.py
@@ -0,0 +1,176 @@
+from pathlib import Path
+from tqdm import tqdm
+import time
+import math
+import psutil
+import random
+import fcntl
+import os
+import subprocess
+import tempfile
+from random import randint
+from errno import EACCES
+
+import hydra
+from omegaconf import DictConfig
+import numpy as np
+import torch
+import pyvista as pv
+
+from pgnd.utils import get_root, mkdir
+from pgnd.ffmpeg import make_video
+
+root: Path = get_root(__file__)
+
+
+class Xvfb(object):
+
+    # Maximum value to use for a display. 32-bit maxint is the
+    # highest Xvfb currently supports
+    MAX_DISPLAY = 2147483647
+    SLEEP_TIME_BEFORE_START = 0.1
+
+    def __init__(
+            self, width=800, height=680, colordepth=24,
+            tempdir=None, display=None, **kwargs):
+        self.width = width
+        self.height = height
+        self.colordepth = colordepth
+        self._tempdir = tempdir or tempfile.gettempdir()
+        self.new_display = display
+
+        if not self.xvfb_exists():
+            msg = (
+                'Can not find Xvfb. Please install it with:\n'
+                '   sudo apt install libgl1-mesa-glx xvfb')
+            raise EnvironmentError(msg)
+
+        self.extra_xvfb_args = ['-screen', '0', '{}x{}x{}'.format(
+                                self.width, self.height, self.colordepth)]
+
+        for key, value in kwargs.items():
+            self.extra_xvfb_args += ['-{}'.format(key), value]
+
+        if 'DISPLAY' in os.environ:
+            self.orig_display = os.environ['DISPLAY'].split(':')[1]
+        else:
+            self.orig_display = None
+
+        self.proc = None
+
+    def __enter__(self):
+        self.start()
+        return self
+
+    def __exit__(self, exc_type, exc_val, exc_tb):
+        self.stop()
+
+    def start(self):
+        if self.new_display is not None:
+            if not self._get_lock_for_display(self.new_display):
+                raise ValueError(f'Could not lock display :{self.new_display}')
+        else:
+            self.new_display = self._get_next_unused_display()
+        display_var = ':{}'.format(self.new_display)
+        self.xvfb_cmd = ['Xvfb', display_var] + self.extra_xvfb_args
+        with open(os.devnull, 'w') as fnull:
+            self.proc = subprocess.Popen(
+                self.xvfb_cmd, stdout=fnull, stderr=fnull, close_fds=True)
+        # give Xvfb time to start
+        time.sleep(self.__class__.SLEEP_TIME_BEFORE_START)
+        ret_code = self.proc.poll()
+        if ret_code is None:
+            self._set_display_var(self.new_display)
+        else:
+            self._cleanup_lock_file()
+            raise RuntimeError(
+                f'Xvfb did not start ({ret_code}): {self.xvfb_cmd}')
+
+    def stop(self):
+        try:
+            if self.orig_display is None:
+                del os.environ['DISPLAY']
+            else:
+                self._set_display_var(self.orig_display)
+            if self.proc is not None:
+                try:
+                    self.proc.terminate()
+                    self.proc.wait()
+                except OSError:
+                    pass
+                self.proc = None
+        finally:
+            self._cleanup_lock_file()
+
+    def xvfb_exists(self):
+        """Check that Xvfb is available on PATH and is executable."""
+        paths = os.environ['PATH'].split(os.pathsep)
+        return any(os.access(os.path.join(path, 'Xvfb'), os.X_OK)
+                   for path in paths)
+
+    def _cleanup_lock_file(self):
+        '''
+        This should always get called if the process exits safely
+        with Xvfb.stop() (whether called explicitly, or by __exit__).
+        If you are ending up with /tmp/X123-lock files when Xvfb is not
+        running, then Xvfb is not exiting cleanly. Always either call
+        Xvfb.stop() in a finally block, or use Xvfb as a context manager
+        to ensure lock files are purged.
+        '''
+        self._lock_display_file.close()
+        try:
+            os.remove(self._lock_display_file.name)
+        except OSError:
+            pass
+
+    def _get_lock_for_display(self, display):
+        '''
+        In order to ensure multi-process safety, this method attempts
+        to acquire an exclusive lock on a temporary file whose name
+        contains the display number for Xvfb.
+        '''
+        tempfile_path = os.path.join(self._tempdir, '.X{0}-lock'.format(display))
+        try:
+            self._lock_display_file = open(tempfile_path, 'w')
+        except PermissionError as e:
+            return False
+        else:
+            try:
+                fcntl.flock(self._lock_display_file,
+                            fcntl.LOCK_EX | fcntl.LOCK_NB)
+            except BlockingIOError:
+                return False
+            else:
+                return True
+
+    def _get_next_unused_display(self):
+        '''
+        Randomly chooses a display number and tries to acquire a lock for this number.
+        If the lock could be acquired, returns this number, otherwise choses a new one.
+        :return: free display number
+        '''
+        while True:
+            rand = randint(1, self.__class__.MAX_DISPLAY)
+            if self._get_lock_for_display(rand):
+                return rand
+            else:
+                continue
+
+    def _set_display_var(self, display):
+        os.environ['DISPLAY'] = ':{}'.format(display)
+
+
+
+def get_camera_custom(center=(0.5, 0.3, 0.5), distance=3.4, azimuth=-125, elevation=30):
+    target = np.array(center)
+    theta = 90 + azimuth
+    y = distance * math.sin(math.radians(elevation))
+    x = math.cos(math.radians(theta)) * distance * math.cos(math.radians(elevation))
+    z = math.sin(math.radians(theta)) * distance * math.cos(math.radians(elevation))
+    origin = target + np.array([x, y, z])
+
+    return [
+        origin.tolist(),
+        target.tolist(),
+        (0.0, 1.0, 0.0),
+    ]
diff --git a/src/experiments/train/train_eval.py b/src/experiments/train/train_eval.py
new file mode 100644
index 0000000000000000000000000000000000000000..36b501d86ebc7e5857442542a50cab05bf279590
--- /dev/null
+++ b/src/experiments/train/train_eval.py
@@ -0,0 +1,782 @@
+from pathlib import Path
+import random
+import time
+import os
+import matplotlib.pyplot as plt
+from collections import defaultdict
+from tqdm import tqdm, trange
+import hydra
+from omegaconf import DictConfig, OmegaConf
+import numpy as np
+from PIL import Image
+import warp as wp
+import matplotlib.pyplot as plt
+import torch
+import torch.backends.cudnn
+import torch.nn as nn
+from torch.nn.utils import clip_grad_norm_
+from torch.utils.data import DataLoader
+import kornia
+import sys
+sys.path.append(str(Path(__file__).parent.parent.parent))
+sys.path.append(str(Path(__file__).parent.parent))
+
+from pgnd.sim import Friction, CacheDiffSimWithFrictionBatch, StaticsBatch, CollidersBatch
+from pgnd.material import PGNDModel
+from pgnd.data import RealTeleopBatchDataset, RealGripperDataset
+from pgnd.utils import Logger, get_root, mkdir
+
+from train.pv_train import do_train_pv
+from train.pv_dataset import do_dataset_pv
+from train.metric_eval import do_metric
+
+root: Path = get_root(__file__)
+
+def dataloader_wrapper(dataloader, name):
+    cnt = 0
+    while True:
+        cnt += 1
+        for data in dataloader:
+            yield data
+
+def transform_gripper_points(cfg, gripper_points, gripper):
+    dx = cfg.sim.num_grids[-1]
+
+    gripper_xyz = gripper[:, :, :, :3]  # (bsz, num_steps, num_grippers, 3)
+    gripper_v = gripper[:, :, :, 3:6]  # (bsz, num_steps, num_grippers, 3)
+    gripper_quat = gripper[:, :, :, 6:10]  # (bsz, num_steps, num_grippers, 4)
+    num_steps = gripper_xyz.shape[1]
+    num_grippers = gripper_xyz.shape[2]
+    gripper_mat = kornia.geometry.conversions.quaternion_to_rotation_matrix(gripper_quat)  # (bsz, num_steps, num_grippers, 3, 3)
+    gripper_points = gripper_points[:, None, None].repeat(1, num_steps, num_grippers, 1, 1)  # (bsz, num_steps, num_grippers, num_points, 3)
+    gripper_x = gripper_points @ gripper_mat + gripper_xyz[:, :, :, None]  # (bsz, num_steps, num_grippers, num_points, 3)
+    bsz = gripper_x.shape[0]
+    num_points = gripper_x.shape[3]
+
+    gripper_quat_vel = gripper[:, :, :, 10:13]  # (bsz, num_steps, num_grippers, 3)
+    gripper_angular_vel = torch.linalg.norm(gripper_quat_vel, dim=-1, keepdims=True)  # (bsz, num_steps, num_grippers, 1)
+    gripper_quat_axis = gripper_quat_vel / (gripper_angular_vel + 1e-10)  # (bsz, num_steps, num_grippers, 3)
+
+    gripper_v_expand = gripper_v[:, :, :, None].repeat(1, 1, 1, num_points, 1)  # (bsz, num_grippers, num_points, 3)
+    gripper_points_from_axis = gripper_x - gripper_xyz[:, :, :, None]  # (bsz, num_steps, num_grippers, num_points, 3)
+    grid_from_gripper_axis = gripper_points_from_axis - \
+        (gripper_quat_axis[:, :, :, None] * gripper_points_from_axis).sum(dim=-1, keepdims=True) * gripper_quat_axis[:, :, :, None]  # (bsz, num_steps, num_grippers, num_particles, 3)
+    gripper_v_expand = torch.cross(gripper_quat_vel[:, :, :, None], grid_from_gripper_axis, dim=-1) + gripper_v_expand
+    gripper_v = gripper_v_expand.reshape(bsz, num_steps, num_grippers * num_points, 3)
+    gripper_x = gripper_x.reshape(bsz, num_steps, num_grippers * num_points, 3)
+
+    gripper_x_mask = (gripper_x[:, :, :, 0] > dx * (cfg.model.clip_bound + 0.5)) \
+                   & (gripper_x[:, :, :, 0] < 1 - (dx * (cfg.model.clip_bound + 0.5))) \
+                   & (gripper_x[:, :, :, 1] > dx * (cfg.model.clip_bound + 0.5)) \
+                   & (gripper_x[:, :, :, 1] < 1 - (dx * (cfg.model.clip_bound + 0.5))) \
+                   & (gripper_x[:, :, :, 2] > dx * (cfg.model.clip_bound + 0.5)) \
+                   & (gripper_x[:, :, :, 2] < 1 - (dx * (cfg.model.clip_bound + 0.5)))
+
+    return gripper_x, gripper_v, gripper_x_mask
+
+
+class Trainer:
+
+    def __init__(self, cfg: DictConfig):
+        self.cfg = cfg
+        print(OmegaConf.to_yaml(cfg, resolve=True))
+
+        wp.init()
+        wp.ScopedTimer.enabled = False
+        wp.set_module_options({'fast_math': False})
+        wp.config.verify_autograd_array_access = True
+
+        gpus = [int(gpu) for gpu in cfg.gpus]
+        wp_devices = [wp.get_device(f'cuda:{gpu}') for gpu in gpus]
+        torch_devices = [torch.device(f'cuda:{gpu}') for gpu in gpus]
+        device_count = len(torch_devices)
+    
+        assert device_count == 1
+        self.wp_device = wp_devices[0]
+        self.torch_device = torch_devices[0]
+
+        seed = cfg.seed
+        random.seed(seed)
+        np.random.seed(seed)
+        torch.manual_seed(seed)
+
+        torch.autograd.set_detect_anomaly(True)
+
+        torch.backends.cudnn.benchmark = True
+
+        # path
+        log_root: Path = root / 'log'
+        exp_root: Path = log_root / cfg.train.name
+        mkdir(exp_root, overwrite=cfg.overwrite, resume=cfg.resume)
+        OmegaConf.save(cfg, exp_root / 'hydra.yaml', resolve=True)
+
+        ckpt_root: Path = exp_root / 'ckpt'
+        ckpt_root.mkdir(parents=True, exist_ok=True)
+        
+        self.log_root = log_root
+        self.ckpt_root = ckpt_root
+
+        self.use_pv = cfg.train.use_pv
+        self.dataset_non_overwrite = cfg.train.dataset_non_overwrite
+        if not self.use_pv:
+            print('not using pv rendering...')
+        
+        assert self.cfg.train.source_dataset_name is not None
+        self.use_gs = cfg.train.use_gs
+
+        # logging
+        self.verbose = False
+        if not cfg.debug:
+            logger = Logger(cfg, project='pgnd-train')
+            self.logger = logger
+
+    def load_train_dataset(self):
+        cfg = self.cfg
+        if cfg.train.dataset_name is None:
+            cfg.train.dataset_name = Path(cfg.train.name).parent / 'dataset'
+
+        source_dataset_root = self.log_root / str(cfg.train.source_dataset_name)
+        assert os.path.exists(source_dataset_root)
+
+        dataset = RealTeleopBatchDataset(
+            cfg, 
+            dataset_root=self.log_root / cfg.train.dataset_name / 'state',
+            source_data_root=source_dataset_root, 
+            device=self.torch_device,
+            num_steps=cfg.sim.num_steps_train,
+            train=True,
+            dataset_non_overwrite=self.dataset_non_overwrite,
+        )
+        self.dataset = dataset
+
+        if cfg.sim.gripper_points:
+            gripper_dataset = RealGripperDataset(
+                cfg,
+                device=self.torch_device,
+                train=True,
+            )
+            self.gripper_dataset = gripper_dataset
+
+    def init_train(self):
+        cfg = self.cfg
+
+        dataloader = dataloader_wrapper(
+            DataLoader(self.dataset, batch_size=cfg.train.batch_size, shuffle=True, num_workers=cfg.train.num_workers, pin_memory=True, drop_last=True),
+            'dataset'
+        )
+        self.dataloader = dataloader
+        if cfg.sim.gripper_points:
+            gripper_dataloader = dataloader_wrapper(
+                DataLoader(self.gripper_dataset, batch_size=cfg.train.batch_size, shuffle=True, num_workers=cfg.train.num_workers, pin_memory=True, drop_last=True),
+                'gripper_dataset'
+            )
+            self.gripper_dataloader = gripper_dataloader 
+
+        # material model
+        material_requires_grad = cfg.model.material.requires_grad
+        material: nn.Module = PGNDModel(cfg)
+        material.to(self.torch_device)
+        material.requires_grad_(material_requires_grad)
+        material.train(True)
+
+        # friction
+        friction: nn.Module = Friction(np.array([cfg.model.friction.value]))
+        friction.to(self.torch_device)
+        friction.requires_grad_(False)
+        friction.train(False)
+
+        if cfg.resume and cfg.train.resume_iteration > 0:
+            assert (self.ckpt_root / f'{cfg.train.resume_iteration:06d}.pt').exists()
+            ckpt = torch.load(self.ckpt_root / f'{cfg.train.resume_iteration:06d}.pt', map_location=self.torch_device)
+            material.load_state_dict(ckpt['material'])
+
+        elif cfg.model.ckpt:
+            ckpt = torch.load(self.log_root / cfg.model.ckpt, map_location=self.torch_device)
+            material.load_state_dict(ckpt['material'])
+
+        if not (cfg.resume and cfg.train.resume_iteration > 0):
+            torch.save({
+                'material': material.state_dict(),
+            }, self.ckpt_root / f'{cfg.train.resume_iteration:06d}.pt')
+
+        if material_requires_grad:
+            material_optimizer = torch.optim.Adam(material.parameters(), lr=cfg.train.material_lr, weight_decay=cfg.train.material_wd)
+            material_lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer=material_optimizer, T_max=cfg.train.num_iterations)
+            if cfg.train.resume_iteration > 0:
+                material_lr_scheduler.last_epoch = cfg.train.resume_iteration - 1
+                material_lr_scheduler.step()
+
+        criterion = nn.MSELoss(reduction='mean')
+        criterion.to(self.torch_device)
+
+        total_step_count = 0
+        if cfg.resume and cfg.train.resume_iteration > 0:
+            total_step_count = cfg.train.resume_iteration * cfg.sim.num_steps_train
+        losses_log = defaultdict(int)
+        loss_factor_v = cfg.train.loss_factor_v
+        loss_factor_x = cfg.train.loss_factor_x
+    
+        self.loss_factor_v = loss_factor_v
+        self.loss_factor_x = loss_factor_x
+        self.material_requires_grad = material_requires_grad
+        self.material = material
+        self.material_optimizer = material_optimizer
+        self.material_lr_scheduler = material_lr_scheduler
+        self.criterion = criterion
+        self.total_step_count = total_step_count
+        self.losses_log = losses_log
+        self.friction = friction
+    
+    def train(self, start_iteration, end_iteration, save=True):
+        cfg = self.cfg
+        self.material.train(True)
+        for iteration in trange(start_iteration, end_iteration, dynamic_ncols=True):
+            if self.material_requires_grad:
+                self.material_optimizer.zero_grad()
+
+            losses = defaultdict(int)
+
+            init_state, actions, gt_states = next(self.dataloader)
+            x, v, x_his, v_his, clip_bound, enabled, episode_vec = init_state
+            x = x.to(self.torch_device)
+            v = v.to(self.torch_device)
+            x_his = x_his.to(self.torch_device)
+            v_his = v_his.to(self.torch_device)
+
+            actions = actions.to(self.torch_device)
+
+            if cfg.sim.gripper_points:
+                gripper_points, _ = next(self.gripper_dataloader)
+                gripper_points = gripper_points.to(self.torch_device)
+                gripper_x, gripper_v, gripper_mask = transform_gripper_points(cfg, gripper_points, actions)  # (bsz, num_steps, num_grippers, 3)
+
+            gt_x, gt_v = gt_states
+            gt_x = gt_x.to(self.torch_device)
+            gt_v = gt_v.to(self.torch_device)
+
+            # gt_x: (bsz, num_steps_total)
+            batch_size = gt_x.shape[0]
+            num_steps_total = gt_x.shape[1]
+            num_particles = gt_x.shape[2]
+
+            if cfg.sim.gripper_points:
+                num_gripper_particles = gripper_x.shape[2]
+                num_particles_orig = num_particles
+                num_particles = num_particles + num_gripper_particles
+
+            sim = CacheDiffSimWithFrictionBatch(cfg, num_steps_total, batch_size, self.wp_device, requires_grad=True)
+
+            statics = StaticsBatch()
+            statics.init(shape=(batch_size, num_particles), device=self.wp_device)
+            statics.update_clip_bound(clip_bound)
+            statics.update_enabled(enabled)
+            colliders = CollidersBatch()
+
+            if cfg.sim.gripper_points:
+                assert not cfg.sim.gripper_forcing
+                num_grippers = 0
+            else:
+                num_grippers = cfg.sim.num_grippers
+
+            colliders.init(shape=(batch_size, num_grippers), device=self.wp_device)
+            if num_grippers > 0:
+                assert len(actions.shape) > 2
+                colliders.initialize_grippers(actions[:, 0])
+
+            enabled = enabled.to(self.torch_device)  # (bsz, num_particles)
+            enabled_mask = enabled.unsqueeze(-1).repeat(1, 1, 3)  # (bsz, num_particles, 3)
+
+            for step in range(num_steps_total):
+                if num_grippers > 0:
+                    colliders.update_grippers(actions[:, step])
+
+                x_in = x.clone()
+                if step == 0:
+                    x_in_gt = x.clone()
+                    v_in_gt = v.clone()
+                else:
+                    x_in_gt = x_in_gt + v_in_gt * cfg.sim.dt * cfg.sim.interval
+
+                if cfg.sim.gripper_points:
+                    x = torch.cat([x, gripper_x[:, step]], dim=1)  # gripper_x: (bsz, num_steps, num_particles, 3)
+                    v = torch.cat([v, gripper_v[:, step]], dim=1)
+                    x_his = torch.cat([x_his, torch.zeros((gripper_x.shape[0], gripper_x.shape[2], cfg.sim.n_history * 3), device=x_his.device, dtype=x_his.dtype)], dim=1)
+                    v_his = torch.cat([v_his, torch.zeros((gripper_x.shape[0], gripper_x.shape[2], cfg.sim.n_history * 3), device=v_his.device, dtype=v_his.dtype)], dim=1)
+                    if enabled.shape[1] < num_particles:
+                        enabled = torch.cat([enabled, gripper_mask[:, step]], dim=1)
+                    statics.update_enabled(enabled.cpu())
+
+                pred = self.material(x, v, x_his, v_his, enabled)
+                x, v = sim(statics, colliders, step, x, v, self.friction.mu.clone()[None].repeat(batch_size, 1), pred)
+
+                if cfg.sim.gripper_forcing:
+                    assert not cfg.sim.gripper_points
+                    gripper_xyz = actions[:, step, :, :3]  # (bsz, num_grippers, 3)
+                    gripper_v = actions[:, step, :, 3:6]  # (bsz, num_grippers, 3)
+                    x_from_gripper = x_in[:, None] - gripper_xyz[:, :, None]  # (bsz, num_grippers, num_particles, 3)
+                    x_gripper_distance = torch.norm(x_from_gripper, dim=-1)  # (bsz, num_grippers, num_particles)
+                    x_gripper_distance_mask = x_gripper_distance < cfg.model.gripper_radius
+                    x_gripper_distance_mask = x_gripper_distance_mask.unsqueeze(-1).repeat(1, 1, 1, 3)  # (bsz, num_grippers, num_particles, 3)
+                    gripper_v_expand = gripper_v[:, :, None].repeat(1, 1, num_particles, 1)  # (bsz, num_grippers, num_particles, 3)
+
+                    gripper_closed = actions[:, step, :, -1] < 0.5  # (bsz, num_grippers)  # 1: open, 0: close
+                    x_gripper_distance_mask = torch.logical_and(x_gripper_distance_mask, gripper_closed[:, :, None, None].repeat(1, 1, num_particles, 3))
+
+                    gripper_quat_vel = actions[:, step, :, 10:13]  # (bsz, num_grippers, 3)
+                    gripper_angular_vel = torch.linalg.norm(gripper_quat_vel, dim=-1, keepdims=True)  # (bsz, num_grippers, 1)
+                    gripper_quat_axis = gripper_quat_vel / (gripper_angular_vel + 1e-10)  # (bsz, num_grippers, 3)
+
+                    grid_from_gripper_axis = x_from_gripper - \
+                        (gripper_quat_axis[:, :, None] * x_from_gripper).sum(dim=-1, keepdims=True) * gripper_quat_axis[:, :, None]  # (bsz, num_grippers, num_particles, 3)
+                    gripper_v_expand = torch.cross(gripper_quat_vel[:, :, None], grid_from_gripper_axis, dim=-1) + gripper_v_expand
+
+                    for i in range(gripper_xyz.shape[1]):
+                        x_gripper_distance_mask_single = x_gripper_distance_mask[:, i]
+                        x[x_gripper_distance_mask_single] = x_in[x_gripper_distance_mask_single] + cfg.sim.dt * gripper_v_expand[:, i][x_gripper_distance_mask_single]
+                        v[x_gripper_distance_mask_single] = gripper_v_expand[:, i][x_gripper_distance_mask_single]
+
+                if cfg.sim.n_history > 0:
+                    if cfg.sim.gripper_points:
+                        x_his_particles = torch.cat([x_his[:, :num_particles_orig].reshape(batch_size, num_particles_orig, -1, 3)[:, :, 1:], x[:, :num_particles_orig, None].detach()], dim=2)
+                        v_his_particles = torch.cat([v_his[:, :num_particles_orig].reshape(batch_size, num_particles_orig, -1, 3)[:, :, 1:], v[:, :num_particles_orig, None].detach()], dim=2)
+                        x_his = x_his_particles.reshape(batch_size, num_particles_orig, -1)
+                        v_his = v_his_particles.reshape(batch_size, num_particles_orig, -1)
+                    else:
+                        x_his = torch.cat([x_his.reshape(batch_size, num_particles, -1, 3)[:, :, 1:], x[:, :, None].detach()], dim=2)
+                        v_his = torch.cat([v_his.reshape(batch_size, num_particles, -1, 3)[:, :, 1:], v[:, :, None].detach()], dim=2)
+                        x_his = x_his.reshape(batch_size, num_particles, -1)
+                        v_his = v_his.reshape(batch_size, num_particles, -1)
+                    
+                if cfg.sim.gripper_points:
+                    x = x[:, :num_particles_orig]
+                    v = v[:, :num_particles_orig]
+                    enabled = enabled[:, :num_particles_orig]
+
+                if self.verbose:
+                    print('x', x.min().item(), x.max().item())
+                    print('v', v.min().item(), v.max().item())
+
+                if self.loss_factor_x > 0:
+                    loss_x = self.criterion(x[enabled_mask > 0], gt_x[:, step][enabled_mask > 0]) * self.loss_factor_x
+                    losses['loss_x'] += loss_x
+                    self.losses_log['loss_x'] += loss_x.item()
+                
+                if self.loss_factor_v > 0:
+                    loss_v = self.criterion(v[enabled_mask > 0], gt_v[:, step][enabled_mask > 0]) * self.loss_factor_v
+                    losses['loss_v'] += loss_v
+                    self.losses_log['loss_v'] += loss_v.item()
+
+                with torch.no_grad():
+                    if self.loss_factor_x > 0:
+                        loss_x_trivial = self.criterion((x_in_gt + v_in_gt * cfg.sim.dt * cfg.sim.interval)[enabled_mask > 0], gt_x[:, step][enabled_mask > 0]) * self.loss_factor_x
+                        self.losses_log['loss_x_trivial'] += loss_x_trivial.item()
+
+                    if self.loss_factor_v > 0:
+                        loss_v_trivial = self.criterion(v_in_gt[enabled_mask > 0], gt_v[:, step][enabled_mask > 0]) * self.loss_factor_v
+                        self.losses_log['loss_v_trivial'] += loss_v_trivial.item()
+
+                    loss_x_sanity = self.criterion(x_in[enabled_mask > 0], (x - v * cfg.sim.dt * cfg.sim.interval)[enabled_mask > 0]) * self.loss_factor_x
+                    self.losses_log['loss_x_sanity'] += loss_x_sanity.item()  # if > 0 then clipping issue
+
+                    if step > 0:
+                        loss_x_gt_sanity = self.criterion((gt_x[:, step - 1] + gt_v[:, step] * cfg.sim.dt * cfg.sim.interval)[enabled_mask > 0], gt_x[:, step][enabled_mask > 0]) * self.loss_factor_x
+                        self.losses_log['loss_x_gt_sanity'] += loss_x_gt_sanity.item()
+                    else:
+                        loss_x_gt_sanity = self.criterion((x_in + gt_v[:, step] * cfg.sim.dt * cfg.sim.interval)[enabled_mask > 0], gt_x[:, step][enabled_mask > 0]) * self.loss_factor_x
+                        self.losses_log['loss_x_gt_sanity'] += loss_x_gt_sanity.item()
+
+                if save and not cfg.debug:
+                    self.logger.add_scalar('main/iteration', iteration, step=self.total_step_count)
+                    for loss_k, loss_v in losses.items():
+                        self.logger.add_scalar(f'main/{loss_k}', loss_v.item(), step=self.total_step_count)
+                self.total_step_count += 1
+
+            loss = sum(losses.values())
+            try:
+                loss.backward()
+            except Exception as e:
+                print(f'loss.backward() failed: {e.with_traceback()}')
+                continue
+
+            if self.material_requires_grad:
+                material_grad_norm = clip_grad_norm_(
+                    self.material.parameters(),
+                    max_norm=cfg.train.material_grad_max_norm,
+                    error_if_nonfinite=True)
+                self.material_optimizer.step()
+
+            if (iteration + 1) % cfg.train.iteration_log_interval == 0:
+                msgs = [
+                    cfg.train.name,
+                    time.strftime('%H:%M:%S'),
+                    'iteration {:{width}d}/{}'.format(iteration + 1, cfg.train.num_iterations, width=len(str(cfg.train.num_iterations))),
+                ]
+
+                msgs.extend([
+                    'pred.norm {:.4f}'.format(pred.norm().item()),
+                ])
+
+                if self.material_requires_grad:
+                    material_lr = self.material_optimizer.param_groups[0]['lr']
+                    msgs.extend([
+                        'e-lr {:.2e}'.format(material_lr),
+                        'e-|grad| {:.4f}'.format(material_grad_norm),
+                    ])
+
+                for loss_k, loss_v in self.losses_log.items():
+                    msgs.append('{} {:.8f}'.format(loss_k, loss_v / cfg.train.iteration_log_interval))
+                    if save and not cfg.debug:
+                        self.logger.add_scalar('stat/mean_{}'.format(loss_k), loss_v / cfg.train.iteration_log_interval, step=self.total_step_count)
+                
+                msg = ','.join(msgs)
+                print('[{}]'.format(msg))
+                self.losses_log = defaultdict(int)
+            
+            if save and not cfg.debug:
+                self.logger.add_scalar('stat/pred_norm', pred.norm().item(), step=self.total_step_count)
+
+            if self.material_requires_grad:
+                material_lr = self.material_optimizer.param_groups[0]['lr']
+                if save and not cfg.debug:
+                    self.logger.add_scalar('stat/material_lr', material_lr, step=self.total_step_count)
+                    self.logger.add_scalar('stat/material_grad_norm', material_grad_norm, step=self.total_step_count)
+
+            if save and (iteration + 1) % cfg.train.iteration_save_interval == 0:
+                torch.save({
+                    'material': self.material.state_dict(),
+                }, self.ckpt_root / '{:06d}.pt'.format(iteration + 1))
+
+            if self.material_requires_grad:
+                self.material_lr_scheduler.step()
+
+
+    def eval_episode(self, iteration: int, episode: int, save: bool = True):
+        cfg = self.cfg
+
+        log_root: Path = root / 'log'
+        eval_name = f'{cfg.train.name}/eval/{cfg.train.dataset_name.split("/")[-1]}/{iteration:06d}'
+        exp_root: Path = log_root / eval_name
+        if save:
+            state_root: Path = exp_root / 'state'
+            mkdir(state_root, overwrite=cfg.overwrite, resume=cfg.resume)
+            episode_state_root = state_root / f'episode_{episode:04d}'
+            mkdir(episode_state_root, overwrite=cfg.overwrite, resume=cfg.resume)
+            OmegaConf.save(cfg, exp_root / 'hydra.yaml', resolve=True)
+
+        if cfg.train.dataset_name is None:
+            cfg.train.dataset_name = Path(cfg.train.name).parent / 'dataset'
+        assert cfg.train.source_dataset_name is not None
+
+        source_dataset_root = self.log_root / str(cfg.train.source_dataset_name)
+        assert os.path.exists(source_dataset_root)
+
+        eval_dataset = RealTeleopBatchDataset(
+            cfg, 
+            dataset_root=self.log_root / cfg.train.dataset_name / 'state',
+            source_data_root=source_dataset_root,
+            device=self.torch_device,
+            num_steps=self.cfg.sim.num_steps,
+            eval_episode_name=f'episode_{episode:04d}',
+        )
+        eval_dataloader = dataloader_wrapper(
+            DataLoader(eval_dataset, batch_size=1, shuffle=False, num_workers=cfg.train.num_workers, pin_memory=True),
+            'dataset'
+        )
+        if cfg.sim.gripper_points:
+            eval_gripper_dataset = RealGripperDataset(
+                cfg,
+                device=self.torch_device,
+            )
+            eval_gripper_dataloader = dataloader_wrapper(
+                DataLoader(eval_gripper_dataset, batch_size=1, shuffle=False, num_workers=cfg.train.num_workers, pin_memory=True),
+                'gripper_dataset'
+            )
+        init_state, actions, gt_states, downsample_indices = next(eval_dataloader)
+
+        x, v, x_his, v_his, clip_bound, enabled, episode_vec = init_state
+        x = x.to(self.torch_device)
+        v = v.to(self.torch_device)
+        x_his = x_his.to(self.torch_device)
+        v_his = v_his.to(self.torch_device)
+    
+        actions = actions.to(self.torch_device)
+
+        if cfg.sim.gripper_points:
+            gripper_points, _ = next(eval_gripper_dataloader)
+            gripper_points = gripper_points.to(self.torch_device)
+            gripper_x, gripper_v, gripper_mask = transform_gripper_points(cfg, gripper_points, actions)  # (bsz, num_steps, num_grippers, 3)
+
+        gt_x, gt_v = gt_states
+        gt_x = gt_x.to(self.torch_device)
+        gt_v = gt_v.to(self.torch_device)
+    
+        # gt_states: (bsz, num_steps_total)
+        batch_size = gt_x.shape[0]
+        num_steps_total = gt_x.shape[1]
+        num_particles = gt_x.shape[2]
+        assert batch_size == 1
+
+        if cfg.sim.gripper_points:
+            num_gripper_particles = gripper_x.shape[2]
+            num_particles_orig = num_particles
+            num_particles = num_particles + num_gripper_particles
+
+        sim = CacheDiffSimWithFrictionBatch(cfg, num_steps_total, batch_size, self.wp_device, requires_grad=True)
+
+        statics = StaticsBatch()
+        statics.init(shape=(batch_size, num_particles), device=self.wp_device)
+        statics.update_clip_bound(clip_bound)
+        statics.update_enabled(enabled)
+        colliders = CollidersBatch()
+        
+        self.material.eval()
+        self.friction.eval()
+
+        if cfg.sim.gripper_points:
+            assert not cfg.sim.gripper_forcing
+            num_grippers = 0
+        else:
+            num_grippers = cfg.sim.num_grippers
+
+        colliders.init(shape=(batch_size, num_grippers), device=self.wp_device)
+        if num_grippers > 0:
+            assert len(actions.shape) > 2
+            colliders.initialize_grippers(actions[:, 0])
+
+        enabled = enabled.to(self.torch_device)
+        enabled_mask = enabled.unsqueeze(-1).repeat(1, 1, 3)  # (bsz, num_particles, 3)
+
+        colliders_save = colliders.export()
+        colliders_save = {key: torch.from_numpy(colliders_save[key])[0].to(x.device).to(x.dtype) for key in colliders_save}
+        ckpt = dict(x=x[0], v=v[0], **colliders_save)
+
+        if save:
+            torch.save(ckpt, episode_state_root / f'{0:04d}.pt')
+
+        losses = {}
+        with torch.no_grad():
+            for step in trange(num_steps_total):
+                if num_grippers > 0:
+                    colliders.update_grippers(actions[:, step])
+                if cfg.sim.gripper_forcing:
+                    x_in = x.clone()
+                else:
+                    x_in = None
+
+                if cfg.sim.gripper_points:
+                    x = torch.cat([x, gripper_x[:, step]], dim=1)  # gripper_points: (bsz, num_steps, num_particles, 3)
+                    v = torch.cat([v, gripper_v[:, step]], dim=1)
+                    x_his = torch.cat([x_his, torch.zeros((gripper_x.shape[0], gripper_x.shape[2], cfg.sim.n_history * 3), device=x_his.device, dtype=x_his.dtype)], dim=1)
+                    v_his = torch.cat([v_his, torch.zeros((gripper_x.shape[0], gripper_x.shape[2], cfg.sim.n_history * 3), device=v_his.device, dtype=v_his.dtype)], dim=1)
+                    if enabled.shape[1] < num_particles:
+                        enabled = torch.cat([enabled, gripper_mask[:, step]], dim=1)
+                    statics.update_enabled(enabled.cpu())
+
+                pred = self.material(x, v, x_his, v_his, enabled)
+
+                if pred.isnan().any():
+                    print('pred isnan', pred.min().item(), pred.max().item())
+                    break
+                if pred.isinf().any():
+                    print('pred isinf', pred.min().item(), pred.max().item())
+                    break
+
+                x, v = sim(statics, colliders, step, x, v, self.friction.mu[None].repeat(batch_size, 1), pred)
+
+                if cfg.sim.gripper_forcing:
+                    assert not cfg.sim.gripper_points
+                    gripper_xyz = actions[:, step, :, :3]  # (bsz, num_grippers, 3)
+                    gripper_v = actions[:, step, :, 3:6]  # (bsz, num_grippers, 3)
+                    x_from_gripper = x_in[:, None] - gripper_xyz[:, :, None]  # (bsz, num_grippers, num_particles, 3)
+                    x_gripper_distance = torch.norm(x_from_gripper, dim=-1)  # (bsz, num_grippers, num_particles)
+                    x_gripper_distance_mask = x_gripper_distance < cfg.model.gripper_radius
+                    x_gripper_distance_mask = x_gripper_distance_mask.unsqueeze(-1).repeat(1, 1, 1, 3)  # (bsz, num_grippers, num_particles, 3)
+                    gripper_v_expand = gripper_v[:, :, None].repeat(1, 1, num_particles, 1)  # (bsz, num_grippers, num_particles, 3)
+
+                    gripper_closed = actions[:, step, :, -1] < 0.5  # (bsz, num_grippers)  # 1: open, 0: close
+                    x_gripper_distance_mask = torch.logical_and(x_gripper_distance_mask, gripper_closed[:, :, None, None].repeat(1, 1, num_particles, 3))
+
+                    gripper_quat_vel = actions[:, step, :, 10:13]  # (bsz, num_grippers, 3)
+                    gripper_angular_vel = torch.linalg.norm(gripper_quat_vel, dim=-1, keepdims=True)  # (bsz, num_grippers, 1)
+                    gripper_quat_axis = gripper_quat_vel / (gripper_angular_vel + 1e-10)  # (bsz, num_grippers, 3)
+
+                    grid_from_gripper_axis = x_from_gripper - \
+                        (gripper_quat_axis[:, :, None] * x_from_gripper).sum(dim=-1, keepdims=True) * gripper_quat_axis[:, :, None]  # (bsz, num_grippers, num_particles, 3)
+                    gripper_v_expand = torch.cross(gripper_quat_vel[:, :, None], grid_from_gripper_axis, dim=-1) + gripper_v_expand
+
+                    for i in range(gripper_xyz.shape[1]):
+                        x_gripper_distance_mask_single = x_gripper_distance_mask[:, i]
+                        x[x_gripper_distance_mask_single] = x_in[x_gripper_distance_mask_single] + cfg.sim.dt * gripper_v_expand[:, i][x_gripper_distance_mask_single]
+                        v[x_gripper_distance_mask_single] = gripper_v_expand[:, i][x_gripper_distance_mask_single]
+                
+                if cfg.sim.n_history > 0:
+                    if cfg.sim.gripper_points:
+                        x_his_particles = torch.cat([x_his[:, :num_particles_orig].reshape(batch_size, num_particles_orig, -1, 3)[:, :, 1:], x[:, :num_particles_orig, None].detach()], dim=2)
+                        v_his_particles = torch.cat([v_his[:, :num_particles_orig].reshape(batch_size, num_particles_orig, -1, 3)[:, :, 1:], v[:, :num_particles_orig, None].detach()], dim=2)
+                        x_his = x_his_particles.reshape(batch_size, num_particles_orig, -1)
+                        v_his = v_his_particles.reshape(batch_size, num_particles_orig, -1)
+                    else:
+                        x_his = torch.cat([x_his.reshape(batch_size, num_particles, -1, 3)[:, :, 1:], x[:, :, None].detach()], dim=2)
+                        v_his = torch.cat([v_his.reshape(batch_size, num_particles, -1, 3)[:, :, 1:], v[:, :, None].detach()], dim=2)
+                        x_his = x_his.reshape(batch_size, num_particles, -1)
+                        v_his = v_his.reshape(batch_size, num_particles, -1)
+
+                if cfg.sim.gripper_points:
+                    extra_save = {
+                        'gripper_x': gripper_x[0, step],
+                        'gripper_v': gripper_v[0, step],
+                        'gripper_actions': actions[0, step],
+                    }
+                    x = x[:, :num_particles_orig]
+                    v = v[:, :num_particles_orig]
+                    enabled = enabled[:, :num_particles_orig]
+                else:
+                    extra_save = {}
+
+                colliders_save = colliders.export()
+                colliders_save = {key: torch.from_numpy(colliders_save[key])[0].to(x.device).to(x.dtype) for key in colliders_save}
+                
+                loss_x = nn.functional.mse_loss(x[enabled_mask > 0], gt_x[:, step][enabled_mask > 0])
+                loss_v = nn.functional.mse_loss(v[enabled_mask > 0], gt_v[:, step][enabled_mask > 0])
+                losses[step] = dict(loss_x=loss_x.item(), loss_v=loss_v.item())
+
+                ckpt = dict(x=x[0], v=v[0], **colliders_save, **extra_save)
+
+                if save and step % cfg.sim.skip_frame == 0:
+                    torch.save(ckpt, episode_state_root / f'{int(step / cfg.sim.skip_frame):04d}.pt')
+
+        metrics = None
+        if save:
+            for loss_k in losses[0].keys():
+                plt.figure(figsize=(10, 5))
+                loss_list = [losses[step][loss_k] for step in losses]
+                plt.plot(loss_list)
+                plt.title(loss_k)
+                plt.grid()
+                plt.savefig(state_root / f'episode_{episode:04d}_{loss_k}.png', dpi=300)
+
+            # particle visualization
+            if self.use_pv:
+                do_train_pv(
+                    cfg,
+                    log_root,
+                    iteration,
+                    [f'episode_{episode:04d}'],
+                    eval_dirname=f'eval',
+                    dataset_name=cfg.train.dataset_name.split("/")[-1],
+                    eval_postfix='',
+                )
+
+            # gaussian splatting visualization
+            if self.use_gs:
+                from .gs import do_gs
+                do_gs(
+                    cfg,
+                    log_root,
+                    iteration,
+                    [f'episode_{episode:04d}'],
+                    eval_dirname=f'eval',
+                    dataset_name=cfg.train.dataset_name.split("/")[-1],
+                    eval_postfix='',
+                    camera_id=1,
+                    with_mask=True,
+                    with_bg=True,
+                )
+
+            # particle visualization of ground truth
+            if self.use_pv:
+                _ = do_dataset_pv(
+                    cfg,
+                    log_root / str(cfg.train.dataset_name),
+                    [f'episode_{episode:04d}'],
+                    save_dir=log_root / f'{cfg.train.name}/eval/{cfg.train.dataset_name.split("/")[-1]}/{iteration:06d}/pv',
+                    downsample_indices=downsample_indices,
+                )
+            
+            metrics = do_metric(
+                cfg,
+                log_root,
+                iteration,
+                [f'episode_{episode:04d}'],
+                downsample_indices,
+                eval_dirname=f'eval',
+                dataset_name=cfg.train.dataset_name.split("/")[-1],
+                eval_postfix='',
+                camera_id=1,
+                use_gs=self.use_gs,
+            )
+
+        return metrics
+
+
+    def eval(self, eval_iteration: int, save: bool = True):
+        cfg = self.cfg
+
+        metrics_list = []
+        start_episode = cfg.train.eval_start_episode
+        end_episode = cfg.train.eval_end_episode if save else cfg.train.eval_start_episode + 2
+        for episode in range(start_episode, end_episode):
+            metrics = self.eval_episode(eval_iteration, episode, save=save)
+            metrics_list.append(metrics)
+
+        if not save:
+            return
+
+        metrics_list = np.array(metrics_list)[:, 0]  # (n_episodes, n_frames, 10 or 3)
+        if self.use_gs:
+            metric_names = ['mse', 'chamfer', 'emd', 'jscore', 'fscore', 'jfscore', 'perception', 'psnr', 'ssim']
+        else:
+            metric_names = ['mse', 'chamfer', 'emd']
+    
+        median_metric = np.median(metrics_list, axis=0)
+        step_75_metric = np.percentile(metrics_list, 75, axis=0)
+        step_25_metric = np.percentile(metrics_list, 25, axis=0)
+        mean_metric = np.mean(metrics_list, axis=0)
+        std_metric = np.std(metrics_list, axis=0)
+
+        for i, metric_name in enumerate(metric_names):
+            # plot error
+            x = np.arange(1, len(median_metric) + 1)
+            plt.figure(figsize=(8, 5))
+            plt.plot(x, median_metric[:, i])
+            plt.xlabel(f"prediction steps, dt={cfg.sim.dt}")
+            plt.ylabel(metric_name)
+            plt.grid()
+
+            ax = plt.gca()
+            x = np.arange(1, len(median_metric) + 1)
+            ax.fill_between(x, step_25_metric[:, i], step_75_metric[:, i], alpha=0.2)
+
+            save_dir = root / 'log' / cfg.train.name / 'eval' / cfg.train.dataset_name.split("/")[-1] / f'{eval_iteration:06d}' / 'metric'
+            plt.tight_layout()
+            plt.savefig(os.path.join(save_dir, f'{i:02d}-{metric_name}.png'), dpi=300)
+            plt.close()
+        
+        # send to wandb
+        if not cfg.debug:
+            for i, metric_name in enumerate(metric_names):
+                self.logger.add_scalar(f'metric/{metric_name}-mean', mean_metric[:, i].mean(), step=self.total_step_count)
+                self.logger.add_scalar(f'metric/{metric_name}-std', std_metric[:, i].mean(), step=self.total_step_count)
+                img = np.array(Image.open(os.path.join(save_dir, f'{i:02d}-{metric_name}.png')).convert('RGB'))
+                self.logger.add_image(f'metric_curve/{metric_name}', img, step=self.total_step_count)
+
+    def test_cuda_mem(self):
+        self.init_train()
+        self.train(0, 10, save=False)
+        self.eval(10, save=False)
+
+@hydra.main(version_base='1.2', config_path=str(root / 'cfg'), config_name='default')
+def main(cfg: DictConfig):
+    trainer = Trainer(cfg)
+    trainer.load_train_dataset()
+    trainer.test_cuda_mem()
+    trainer.init_train()
+    for iteration in range(cfg.train.resume_iteration, cfg.train.num_iterations, cfg.train.iteration_eval_interval):
+        start_iteration = iteration
+        end_iteration = min(iteration + cfg.train.iteration_eval_interval, cfg.train.num_iterations)
+        trainer.train(start_iteration, end_iteration)
+        trainer.eval(end_iteration)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/src/pgnd/__init__.py b/src/pgnd/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..3bcd6674d667ec1683bb72fd60cf56996e16cbf9
--- /dev/null
+++ b/src/pgnd/__init__.py
@@ -0,0 +1,9 @@
+import numpy
+numpy.bool = bool  # to avoid deprecation error
+
+from . import utils
+from . import ffmpeg
+
+from . import data
+from . import material
+from . import sim
diff --git a/src/pgnd/data/__init__.py b/src/pgnd/data/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..ec7075c7b7a5a775274270d522c1d374451e07d1
--- /dev/null
+++ b/src/pgnd/data/__init__.py
@@ -0,0 +1,2 @@
+from .dataset import *
+from .dataset_gripper import *
diff --git a/src/pgnd/data/dataset.py b/src/pgnd/data/dataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..15f3ea2b7dca6c1f77602fc31f3c0b6c958e5739
--- /dev/null
+++ b/src/pgnd/data/dataset.py
@@ -0,0 +1,369 @@
+from pathlib import Path
+from typing import Union, Optional
+from omegaconf import DictConfig
+
+import os
+import torch
+import time
+import shutil
+import json
+import yaml
+import random
+import glob
+import kornia
+import numpy as np
+import pickle as pkl
+import open3d as o3d
+from torch.utils.data import Dataset
+from dgl.geometry import farthest_point_sampler
+from sklearn.neighbors import NearestNeighbors
+
+
+
+def preprocess(cfg, dataset_root_episode, source_data_root_episode, dt_frame, dx):
+    print('Preprocessing episode:', dataset_root_episode.name)
+    assert dt_frame - 1. / 30. < 1e-3
+
+    # transform xyz: x -> x, z -> -y, y -> z
+    R = np.array(
+        [[1, 0, 0],
+         [0, 0, -1],
+         [0, 1, 0]]
+    )
+
+    eef_global_T = np.array([cfg.model.eef_t[0], cfg.model.eef_t[1], cfg.model.eef_t[2]])  # 1018_sloth: 0.185, 1018_rope_short: 0.01
+
+    xyz = np.load(source_data_root_episode / 'traj.npz')['xyz']
+    eef_xyz = np.loadtxt(source_data_root_episode / 'eef_traj.txt')
+    n_frames = eef_xyz.shape[0]
+    eef_xyz = eef_xyz.reshape(eef_xyz.shape[0], -1, 3)  # (n_frames, n_grippers, 3)
+    eef_xyz = eef_xyz + eef_global_T
+
+    xyz = np.einsum('nij,jk->nik', xyz, R.T)
+    eef_xyz = np.einsum('nij,jk->nik', eef_xyz, R.T)  # (n_frames, n_grippers, 3)
+
+    if os.path.exists(source_data_root_episode / 'eef_rot.txt'):
+        eef_rot = np.loadtxt(source_data_root_episode / 'eef_rot.txt')  # (n_frames, -1)
+        eef_rot = eef_rot.reshape(eef_rot.shape[0], -1, 3, 3)  # (n_frames, n_grippers, 3, 3)
+        eef_rot = R @ eef_rot @ R.T
+        eef_rot = torch.from_numpy(eef_rot).reshape(-1, 3, 3)  # (n_frames, n_grippers, 3, 3)
+        eef_quat = kornia.geometry.conversions.rotation_matrix_to_quaternion(eef_rot).numpy().reshape(n_frames, -1, 4)  # (n_frames, n_grippers, 4)
+    else:
+        eef_quat = np.zeros((xyz.shape[0], cfg.sim.num_grippers, 4), dtype=np.float32)  # (n_frames, n_grippers, 4)
+        eef_quat[:, :, 0] = 1.0  # (n_frames, n_grippers, 4)
+
+    if os.path.exists(source_data_root_episode / 'eef_gripper.txt'):
+        eef_gripper = np.loadtxt(source_data_root_episode / 'eef_gripper.txt')  # (n_frames, n_grippers)
+        eef_gripper = eef_gripper.reshape(eef_gripper.shape[0], -1)
+        assert cfg.sim.num_grippers == eef_gripper.shape[1]
+    else:
+        eef_gripper = np.zeros((n_frames, cfg.sim.num_grippers), dtype=np.float32)  # (n_frames, n_grippers)
+    assert cfg.sim.num_grippers == eef_quat.shape[1]
+
+    scale = cfg.sim.preprocess_scale
+    xyz = xyz * scale
+    eef_xyz = eef_xyz * scale
+
+    n_frames = xyz.shape[0]
+
+    # construct colliders: position, velocity, height, radius, axis
+    eef_vel = np.zeros_like(eef_xyz)  # (n_frames, n_grippers, 3)
+    eef_vel[:-1] = (eef_xyz[1:] - eef_xyz[:-1]) / dt_frame
+    eef_vel[-1] = eef_vel[-2]
+
+    # construct rotational velocity
+    eef_rot_this = kornia.geometry.conversions.quaternion_to_rotation_matrix(torch.from_numpy(eef_quat[:-1]).reshape(-1, 4))  # (n_frames-1 * n_grippers, 3, 3)
+    eef_rot_next = kornia.geometry.conversions.quaternion_to_rotation_matrix(torch.from_numpy(eef_quat[1:]).reshape(-1, 4))  # (n_frames-1 * n_grippers, 3, 3)
+    eef_rot_delta = eef_rot_this.bmm(eef_rot_next.inverse())
+    eef_aa = kornia.geometry.conversions.rotation_matrix_to_axis_angle(eef_rot_delta)  # (n_frames-1 * n_grippers, 3)
+
+    eef_quat_vel = np.zeros((n_frames, cfg.sim.num_grippers, 3), dtype=eef_quat.dtype)
+    eef_quat_vel[:-1] = eef_aa.reshape(n_frames - 1, -1, 3) / dt_frame  # (n_frames-1, n_grippers, 3), radian per second
+    eef_quat_vel[-1] = eef_quat_vel[-2]
+
+    grippers = np.zeros((n_frames, cfg.sim.num_grippers, 15))
+    grippers[:, :, :3] = eef_xyz
+    grippers[:, :, 3:6] = eef_vel
+    grippers[:, :, 6:10] = eef_quat
+    grippers[:, :, 10:13] = eef_quat_vel
+    grippers[:, :, 13] = cfg.model.gripper_radius
+    grippers[:, :, 14] = eef_gripper
+    
+    if cfg.sim.preprocess_with_table:  # drop everything to the table plane (y=0)
+        global_translation = np.array([
+            0.5 - (xyz[:, :, 0].max() + xyz[:, :, 0].min()) / 2,
+            dx * (cfg.model.clip_bound + 0.5) + 1e-5 - xyz[:, :, 1].min(),
+            0.5 - (xyz[:, :, 2].max() + xyz[:, :, 2].min()) / 2,
+        ], dtype=xyz.dtype)
+    else:
+        global_translation = np.array([
+            0.5 - (xyz[:, :, 0].max() + xyz[:, :, 0].min()) / 2,
+            0.5 - (xyz[:, :, 1].max() + xyz[:, :, 1].min()) / 2,
+            0.5 - (xyz[:, :, 2].max() + xyz[:, :, 2].min()) / 2,
+        ], dtype=xyz.dtype)
+    xyz += global_translation
+    grippers[:, :, :3] += global_translation
+    
+    if not (xyz[:, :, 0].min() >= dx * (cfg.model.clip_bound + 0.5) - 1e-6 \
+        and xyz[:, :, 0].max() <= 1 - dx * (cfg.model.clip_bound + 0.5) + 1e-6 \
+        and xyz[:, :, 1].min() >= dx * (cfg.model.clip_bound + 0.5) - 1e-6 \
+        and xyz[:, :, 1].max() <= 1 - dx * (cfg.model.clip_bound + 0.5) + 1e-6 \
+        and xyz[:, :, 2].min() >= dx * (cfg.model.clip_bound + 0.5) - 1e-6 \
+        and xyz[:, :, 2].max() <= 1 - dx * (cfg.model.clip_bound + 0.5) + 1e-6):
+        print(dataset_root_episode.name, 'out of bound')
+        xyz_max = xyz.max(axis=0)
+        xyz_max_mask = (xyz_max[:, 0] > 1 - dx * (cfg.model.clip_bound + 0.5) + 1e-6) | \
+            (xyz_max[:, 1] > 1 - dx * (cfg.model.clip_bound + 0.5) + 1e-6) | \
+            (xyz_max[:, 2] > 1 - dx * (cfg.model.clip_bound + 0.5) + 1e-6)
+        xyz_min = xyz.min(axis=0)
+        xyz_min_mask = (xyz_min[:, 0] < dx * (cfg.model.clip_bound + 0.5) - 1e-6) | \
+            (xyz_min[:, 1] < dx * (cfg.model.clip_bound + 0.5) - 1e-6) | \
+            (xyz_min[:, 2] < dx * (cfg.model.clip_bound + 0.5) - 1e-6)
+        xyz_mask = xyz_max_mask | xyz_min_mask
+        xyz = xyz[:, ~xyz_mask]
+        
+        assert (xyz[:, :, 0].min() >= dx * (cfg.model.clip_bound + 0.5) - 1e-6 \
+            and xyz[:, :, 0].max() <= 1 - dx * (cfg.model.clip_bound + 0.5) + 1e-6 \
+            and xyz[:, :, 1].min() >= dx * (cfg.model.clip_bound + 0.5) - 1e-6 \
+            and xyz[:, :, 1].max() <= 1 - dx * (cfg.model.clip_bound + 0.5) + 1e-6 \
+            and xyz[:, :, 2].min() >= dx * (cfg.model.clip_bound + 0.5) - 1e-6 \
+            and xyz[:, :, 2].max() <= 1 - dx * (cfg.model.clip_bound + 0.5) + 1e-6)
+
+    data = {
+        'num_particles': xyz.shape[1],
+        'clip_bound': cfg.model.clip_bound,
+    }
+    with open(dataset_root_episode / 'info.yaml', 'w') as f:
+        yaml.dump(data, f)
+    
+    # save data
+    for frame_id in range(n_frames):
+        data = {
+            'x': torch.tensor(xyz[frame_id]).float(),
+            'grippers': torch.tensor(grippers[frame_id]).float()
+        }
+        torch.save(data, dataset_root_episode / f'{frame_id:04d}.pt')
+    return True  # necessary for success check
+
+
+def fps(x, enabled, n, device, random_start=False):
+    assert torch.diff(enabled * 1.0).sum() in [0.0, -1.0]
+    start_idx = random.randint(0, enabled.sum() - 1) if random_start else 0
+    fps_idx = farthest_point_sampler(x[enabled][None], n, start_idx=start_idx)[0]
+    fps_idx = fps_idx.to(x.device)
+    return fps_idx
+
+
+class RealTeleopBatchDataset(Dataset):
+
+    def __init__(self,
+                cfg: DictConfig, 
+                dataset_root: Union[str, Path],
+                source_data_root: Union[str, Path], 
+                device: torch.device, 
+                num_steps: int, 
+                train=False, 
+                eval_episode_name=None, 
+                dataset_non_overwrite=None
+        ) -> None:
+        super().__init__()
+        self.device = device
+        dataset_root = Path(dataset_root).resolve()
+        dataset_root.mkdir(parents=True, exist_ok=True)
+        self.root = dataset_root
+        self.is_train = train
+
+        self.interval = cfg.sim.interval
+        self.dt = eval(cfg.sim.dt) if isinstance(cfg.sim.dt, str) else cfg.sim.dt
+        self.num_steps = num_steps
+        self.skip_frame = cfg.train.dataset_skip_frame
+        self.n_particles = cfg.sim.n_particles
+        self.n_history = cfg.sim.n_history
+        self.uniform = cfg.sim.uniform or not train  # always use uniform sampling during test
+        print('Using uniform sampling:', self.uniform)
+        self.load_skip_frame = cfg.train.dataset_load_skip_frame
+        self.dx = cfg.sim.num_grids[-1]
+
+        self.return_downsample_indices = (eval_episode_name is not None)
+
+        with torch.no_grad():
+            source_data_root = Path(source_data_root).resolve()
+            episodes = list(sorted(Path(str(source_data_root)).glob('episode_*')))
+            if len(episodes) == 0:
+                import ipdb; ipdb.set_trace()
+            if eval_episode_name is not None:
+                assert not train
+                episodes = [e for e in episodes if e.name == eval_episode_name]
+            if train:
+                episodes = episodes[cfg.train.training_start_episode:cfg.train.training_end_episode]
+
+            episodes_clean = []
+            episodes_xs = []
+            episodes_vs = []
+            episodes_enabled = []
+            episodes_grippers = []
+            episodes_clip_bound = []
+            len_sim_list = []
+            traj_list = []
+            meta_list = []
+            use_grippers = False
+            max_num_particles = 0
+            max_num_frames = 0
+            overwrite = None if eval_episode_name is None else True
+
+            for episode_id in range(len(episodes)):
+                source_data_root_episode = episodes[episode_id]
+                episode_name = source_data_root_episode.name
+                dataset_root_episode = dataset_root / episode_name
+                if os.path.exists(dataset_root_episode):
+                    traj = list(sorted(dataset_root_episode.glob('*.pt')))[::self.load_skip_frame]
+                    if len(traj) > 0:
+                        if dataset_non_overwrite:
+                            overwrite = False
+                        while overwrite is None:
+                            feedback = input(f'{dataset_root_episode} already exists, overwrite? [y/n] ')
+                            ret = feedback.casefold()
+                            if ret == 'y':
+                                overwrite = True
+                                break
+                            elif ret == 'n':
+                                overwrite = False
+                                break
+                        if overwrite == True: 
+                            shutil.rmtree(dataset_root_episode, ignore_errors=True)
+                        else:
+                            assert overwrite == False
+                            episodes_clean.append(episode_id)
+                    elif overwrite is None: overwrite = True
+                    elif overwrite == False: continue
+                elif overwrite is None: overwrite = True
+                elif overwrite == False: continue
+                if overwrite:
+                    dataset_root_episode.mkdir(parents=True, exist_ok=True)
+                    success = preprocess(cfg, dataset_root_episode, source_data_root_episode,
+                            self.dt * self.interval / self.skip_frame / self.load_skip_frame, self.dx)
+                    if not success:
+                        shutil.rmtree(dataset_root_episode, ignore_errors=True)
+                        continue
+                    else:
+                        episodes_clean.append(episode_id)
+                    traj = list(sorted(dataset_root_episode.glob('*.pt')))[::self.load_skip_frame]
+
+                states = [torch.load(p, map_location='cpu') for p in traj]
+
+                xs = torch.stack([state['x'] for state in states], dim=0)
+                vs = torch.zeros_like(xs)
+                vs[1:] = (xs[1:] - xs[:-1]) / (self.dt * self.interval / self.skip_frame) #  / self.load_skip_frame)
+                vs[0] = vs[1]
+                traj_list.append(traj)
+
+                num_particles = xs.size(1)
+                max_num_particles = max(max_num_particles, num_particles)
+                max_num_frames = max(len(traj), max_num_frames)
+
+                len_sim = max(1, len(traj) - (self.num_steps + self.n_history) * self.skip_frame)
+                len_sim_list.append(len_sim)
+
+                episodes_xs.append(xs)
+                episodes_vs.append(vs)
+
+                grippers = torch.stack([state['grippers'] for state in states], dim=0)
+                episodes_grippers.append(grippers)
+
+                # load yaml
+                yaml_path = dataset_root_episode / 'info.yaml'
+                with open(yaml_path, 'r') as f:
+                    info = yaml.safe_load(f)
+                clip_bound = info['clip_bound']
+                episodes_clip_bound.append(clip_bound)
+            
+            for episode_id in range(len(episodes_clean)):
+                xs = episodes_xs[episode_id]
+                vs = episodes_vs[episode_id]
+                enabled = torch.ones(xs.size(1), dtype=torch.bool)
+                num_particles_pad = max_num_particles - xs.size(1)
+                num_frames_pad = max_num_frames - xs.size(0)
+                xs = torch.nn.functional.pad(xs, (0, 0, 0, num_particles_pad, 0, num_frames_pad), value=0)
+                vs = torch.nn.functional.pad(vs, (0, 0, 0, num_particles_pad, 0, num_frames_pad), value=0)
+                enabled = torch.nn.functional.pad(enabled, (0, num_particles_pad), value=0)
+                episodes_xs[episode_id] = xs
+                episodes_vs[episode_id] = vs
+                episodes_enabled.append(enabled)
+                
+                grippers = episodes_grippers[episode_id]
+                grippers = torch.nn.functional.pad(grippers, (0, 0, 0, 0, 0, num_frames_pad), value=0)
+                episodes_grippers[episode_id] = grippers
+
+            self.episode_xs = torch.stack(episodes_xs, dim=0)  # (n_episodes, n_frames, n_particles, 3)
+            self.episode_vs = torch.stack(episodes_vs, dim=0)
+            self.episode_enabled = torch.stack(episodes_enabled, dim=0)  # (n_episodes, n_particles)
+            self.episode_grippers = torch.stack(episodes_grippers, dim=0)
+            self.episode_clip_bound = torch.tensor(episodes_clip_bound, dtype=torch.float32)
+            self.len_sim_list = np.array(len_sim_list)
+            self.total_len_sim = sum(len_sim_list)
+            self.max_num_particles = max_num_particles
+            self.traj_list = traj_list
+            self.meta_list = meta_list
+        
+        print('Finished loading dataset')
+        print('Total number of episodes:', len(episodes_clean))
+        print('Total number of frames:', self.total_len_sim)
+        assert len(self.episode_xs) == len(episodes_clean)
+
+    def __len__(self) -> int:
+        return self.total_len_sim
+
+
+    def __getitem__(self, index):
+        cum_len_sim = np.cumsum(self.len_sim_list)
+        episode = np.where(cum_len_sim - index > 0)[0][0]
+        frame = index - cum_len_sim[episode - 1] if episode > 0 else index
+
+        # history frames
+        frame = frame + self.n_history * self.skip_frame
+
+        x = self.episode_xs[episode][frame]
+        v = self.episode_vs[episode][frame]  # velocity from step to step+1
+        enabled = self.episode_enabled[episode]
+
+        # downsample
+        if self.uniform:
+            downsample_indices = fps(x, enabled, self.n_particles, self.device, random_start=True)
+        else:
+            downsample_indices = torch.randperm(enabled.sum())[:self.n_particles]
+        x = x[downsample_indices]
+        v = v[downsample_indices]
+
+        x_his = torch.zeros((self.n_particles, 0), dtype=x.dtype, device=x.device)
+        v_his = torch.zeros((self.n_particles, 0), dtype=v.dtype, device=v.device)
+        for his_id in range(1, self.n_history + 1):
+            his_frame = frame - his_id * self.skip_frame
+            assert his_frame >= 0
+            x_his_frame = self.episode_xs[episode][his_frame]
+            v_his_frame = self.episode_vs[episode][his_frame]
+            x_his_frame = x_his_frame[downsample_indices]
+            v_his_frame = v_his_frame[downsample_indices]
+            x_his = torch.cat([x_his, x_his_frame], dim=-1)
+            v_his = torch.cat([v_his, v_his_frame], dim=-1)
+    
+        enabled = torch.ones(x.size(0), dtype=torch.bool)
+        clip_bound = self.episode_clip_bound[episode]
+        episode_vec = torch.tensor([episode, frame])
+        init_state = (x, v, x_his, v_his, clip_bound, enabled, episode_vec)
+
+        end_frame = frame + self.num_steps * self.skip_frame + 1
+
+        actions = self.episode_grippers[episode][frame+self.skip_frame:end_frame:self.skip_frame]
+
+        gt_xs = self.episode_xs[episode][frame+self.skip_frame:end_frame:self.skip_frame]
+        gt_vs = self.episode_vs[episode][frame+self.skip_frame:end_frame:self.skip_frame]
+        
+        gt_xs = gt_xs[:, downsample_indices]
+        gt_vs = gt_vs[:, downsample_indices]
+        
+        gt_states = (gt_xs, gt_vs)
+
+        if self.return_downsample_indices:
+            return init_state, actions, gt_states, downsample_indices
+        else:
+            return init_state, actions, gt_states
diff --git a/src/pgnd/data/dataset_gripper.py b/src/pgnd/data/dataset_gripper.py
new file mode 100644
index 0000000000000000000000000000000000000000..5eae193777ce3ca40ac13e369a3e83fded5202a0
--- /dev/null
+++ b/src/pgnd/data/dataset_gripper.py
@@ -0,0 +1,99 @@
+from pathlib import Path
+from typing import Union, Optional
+from omegaconf import DictConfig
+
+import os
+import torch
+import time
+import shutil
+import json
+import yaml
+import random
+import glob
+import kornia
+import numpy as np
+import pickle as pkl
+import open3d as o3d
+from torch.utils.data import Dataset
+from dgl.geometry import farthest_point_sampler
+from sklearn.neighbors import NearestNeighbors
+
+
+def fps(x, n, device, random_start=False):
+    start_idx = random.randint(0, x.shape[0] - 1) if random_start else 0
+    fps_idx = farthest_point_sampler(x[None], n, start_idx=start_idx)[0]
+    fps_idx = fps_idx.to(x.device)
+    return fps_idx
+
+
+def read_splat(splat_file):
+    with open(splat_file, "rb") as f:
+        data = f.read()
+    pts = []
+    colors = []
+    scales = []
+    quats = []
+    opacities = []
+    for i in range(0, len(data), 32):
+        v = np.frombuffer(data[i : i + 12], dtype=np.float32)
+        s = np.frombuffer(data[i + 12 : i + 24], dtype=np.float32)
+        c = np.frombuffer(data[i + 24 : i + 28], dtype=np.uint8) / 255
+        q = np.frombuffer(data[i + 28 : i + 32], dtype=np.uint8)
+        q = (q * 1.0 - 128) / 128
+        pts.append(v)
+        scales.append(s)
+        colors.append(c[:3])
+        quats.append(q)
+        opacities.append(c[3:])
+    return np.array(pts), np.array(colors), np.array(scales), np.array(quats), np.array(opacities)
+
+
+class RealGripperDataset(Dataset):
+
+    def __init__(self, cfg: DictConfig, device: torch.device, train=False) -> None:
+        super().__init__()
+
+        pts, colors, scales, quats, opacities = read_splat('experiments/log/gs/ckpts/gripper_new.splat')
+        self.device = device
+        self.uniform = cfg.sim.uniform or not train
+        self.n_particles = 500
+
+        R = np.array(
+            [[1, 0, 0],
+            [0, 0, -1],
+            [0, 1, 0]]
+        )
+        eef_global_T = np.array([cfg.model.eef_t[0], cfg.model.eef_t[1], cfg.model.eef_t[2]])  # 1018_sloth: 0.185, 1018_rope_short: 0.013)
+        pts = pts + eef_global_T
+        pts = pts @ R.T
+        scale = cfg.sim.preprocess_scale
+        pts = pts * scale
+
+        axis = np.array([0, 1, 0])
+        angle = -27  # degree
+        R = o3d.geometry.get_rotation_matrix_from_axis_angle(axis * np.pi / 180 * angle)
+        translation = np.array([-0.015, 0.06, 0])
+
+        pts = pts @ R.T
+        pts = pts + translation
+
+        R = np.array(
+            [[0, 0, 1],
+            [0, 1, 0],
+            [-1, 0, 0]]
+        )
+        pts = pts @ R.T
+
+        self.gripper_pts = torch.from_numpy(pts).to(torch.float32)
+
+    def __len__(self) -> int:
+        return 10000000
+
+    def __getitem__(self, index):
+        x = self.gripper_pts
+        if self.uniform:
+            downsample_indices = fps(x, self.n_particles, self.device, random_start=True)
+        else:
+            downsample_indices = torch.randperm(x.shape[0])[:self.n_particles]
+        x = x[downsample_indices]
+        return x, downsample_indices
diff --git a/src/pgnd/ffmpeg.py b/src/pgnd/ffmpeg.py
new file mode 100644
index 0000000000000000000000000000000000000000..731e3dd60a09ae3aaf298629771998367f4dcb48
--- /dev/null
+++ b/src/pgnd/ffmpeg.py
@@ -0,0 +1,47 @@
+from pathlib import Path
+import subprocess
+
+
+def make_video(
+        image_root: Path,
+        video_path: Path,
+        image_pattern: str = '%04d.png',
+        frame_rate: int = 10):
+
+    subprocess.run([
+        'ffmpeg',
+        '-y',
+        '-hide_banner',
+        '-loglevel', 'error',
+        '-framerate', str(frame_rate),
+        '-i', str(image_root / image_pattern),
+        '-c:v', 'libx264',
+        '-pix_fmt', 'yuv420p',
+        str(video_path)
+    ])
+
+
+def cat_videos(input_videos: list[Path], output_video: Path):
+
+    output_video.parent.mkdir(parents=True, exist_ok=True)
+
+    num_videos = len(input_videos)
+
+    if num_videos <= 1:
+        raise ValueError('concatenating <=1 videos')
+
+    video_args = []
+    for input_video in input_videos:
+        video_args.extend(['-i', str(input_video)])
+
+    subprocess.run([
+        'ffmpeg',
+        '-y',
+        '-hide_banner',
+        '-loglevel', 'error',
+    ] + video_args + [
+        '-filter_complex',
+        '{}hstack=inputs={}[v]'.format(''.join([f'[{i}:v]' for i in range(num_videos)]), num_videos),
+        '-map', '[v]',
+        str(output_video)
+    ])
diff --git a/src/pgnd/material/__init__.py b/src/pgnd/material/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..177e88e5dfe0d7607a493206fd3b7d07882b834c
--- /dev/null
+++ b/src/pgnd/material/__init__.py
@@ -0,0 +1 @@
+from .pgnd import *
diff --git a/src/pgnd/material/network/__init__.py b/src/pgnd/material/network/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/src/pgnd/material/network/nerf.py b/src/pgnd/material/network/nerf.py
new file mode 100644
index 0000000000000000000000000000000000000000..09e6a824857576af55cd7d7935a7c2f8f58d9cdd
--- /dev/null
+++ b/src/pgnd/material/network/nerf.py
@@ -0,0 +1,54 @@
+import torch
+import torch.nn as nn
+
+
+class CondNeRFModel(torch.nn.Module):
+    def __init__(
+        self,
+        xyz_dim=3,
+        condition_dim=64,
+        out_channel=3,
+        num_layers=8,
+        hidden_size=256,
+        skip_connect_every=4,
+    ):
+        super(CondNeRFModel, self).__init__()
+
+        self.dim_xyz = xyz_dim
+        self.dim_cond = condition_dim
+        self.skip_connect_every = skip_connect_every
+
+        self.layer1 = torch.nn.Linear(self.dim_xyz + self.dim_cond, hidden_size)
+        
+        self.layers = torch.nn.ModuleList()
+        for i in range(num_layers - 1):
+            if self.skip_connect_every is not None and i % self.skip_connect_every == 0 and i > 0 and i != num_layers - 1:
+                self.layers.append(torch.nn.Linear(self.dim_xyz + self.dim_cond + hidden_size, hidden_size))
+            else:
+                self.layers.append(torch.nn.Linear(hidden_size, hidden_size))
+
+        self.layers_out = torch.nn.ModuleList([
+            torch.nn.Linear(hidden_size, hidden_size // 2),
+            torch.nn.Linear(hidden_size // 2, out_channel),
+        ])
+        self.relu = nn.ReLU()
+
+    def forward(self, xyz, cond):
+        assert xyz.shape[1] == self.dim_xyz
+        xyz = xyz[..., :self.dim_xyz]
+        xyz = torch.cat([xyz, cond], 1)
+        x = self.layer1(xyz)
+        for i in range(len(self.layers)):
+            if (
+                self.skip_connect_every is not None
+                and i % self.skip_connect_every == 0
+                and i > 0
+                and i != len(self.layers) - 1
+            ):
+                x = torch.cat((x, xyz), dim=-1)
+            x = self.relu(self.layers[i](x))
+        for i in range(len(self.layers_out) - 1):
+            x = self.relu(self.layers_out[i](x))
+        x = self.layers_out[-1](x)
+        return x
+
diff --git a/src/pgnd/material/network/pointnet.py b/src/pgnd/material/network/pointnet.py
new file mode 100644
index 0000000000000000000000000000000000000000..833501d330f02750410616ff6f03a46bfd53f5cc
--- /dev/null
+++ b/src/pgnd/material/network/pointnet.py
@@ -0,0 +1,150 @@
+import torch
+import torch.nn as nn
+import torch.nn.parallel
+import torch.utils.data
+from torch.autograd import Variable
+import numpy as np
+import torch.nn.functional as F
+
+
+class STN3d(nn.Module):
+    def __init__(self, channel):
+        super(STN3d, self).__init__()
+        self.conv1 = torch.nn.Conv1d(channel, 64, 1)
+        self.conv2 = torch.nn.Conv1d(64, 128, 1)
+        self.conv3 = torch.nn.Conv1d(128, 1024, 1)
+        self.fc1 = nn.Linear(1024, 512)
+        self.fc2 = nn.Linear(512, 256)
+        self.fc3 = nn.Linear(256, 9)
+        self.relu = nn.ReLU()
+
+        self.bn1 = nn.BatchNorm1d(64)
+        self.bn2 = nn.BatchNorm1d(128)
+        self.bn3 = nn.BatchNorm1d(1024)
+        self.bn4 = nn.BatchNorm1d(512)
+        self.bn5 = nn.BatchNorm1d(256)
+
+    def forward(self, x, x_mask):
+        batchsize = x.size()[0]
+        x_mask = x_mask.unsqueeze(1)  # B, N -> B, 1, N
+        x = F.relu(self.bn1(self.conv1(x)))
+        x = F.relu(self.bn2(self.conv2(x)))
+        x = F.relu(self.bn3(self.conv3(x)))
+        x = x * x_mask - (~x_mask) * 1e10
+        x = torch.max(x, 2, keepdim=True)[0]
+        assert x.min() > -1e10
+        x = x.view(-1, 1024)
+
+        x = F.relu(self.bn4(self.fc1(x)))
+        x = F.relu(self.bn5(self.fc2(x)))
+        x = self.fc3(x)
+
+        iden = Variable(torch.from_numpy(np.array([1, 0, 0, 0, 1, 0, 0, 0, 1]).astype(np.float32))).view(1, 9).repeat(
+            batchsize, 1)
+        if x.is_cuda:
+            iden = iden.cuda()
+        x = x + iden
+        x = x.view(-1, 3, 3)
+        return x
+
+
+class STNkd(nn.Module):
+    def __init__(self, k=64):
+        super(STNkd, self).__init__()
+        self.conv1 = torch.nn.Conv1d(k, 64, 1)
+        self.conv2 = torch.nn.Conv1d(64, 128, 1)
+        self.conv3 = torch.nn.Conv1d(128, 1024, 1)
+        self.fc1 = nn.Linear(1024, 512)
+        self.fc2 = nn.Linear(512, 256)
+        self.fc3 = nn.Linear(256, k * k)
+        self.relu = nn.ReLU()
+
+        self.bn1 = nn.BatchNorm1d(64)
+        self.bn2 = nn.BatchNorm1d(128)
+        self.bn3 = nn.BatchNorm1d(1024)
+        self.bn4 = nn.BatchNorm1d(512)
+        self.bn5 = nn.BatchNorm1d(256)
+
+        self.k = k
+
+    def forward(self, x, x_mask):
+        batchsize = x.size()[0]
+        x_mask = x_mask.unsqueeze(1)
+        x = F.relu(self.bn1(self.conv1(x)))
+        x = F.relu(self.bn2(self.conv2(x)))
+        x = F.relu(self.bn3(self.conv3(x)))
+        x = x * x_mask - (~x_mask) * 1e10
+        x = torch.max(x, 2, keepdim=True)[0]
+        assert x.min() > -1e10
+        x = x.view(-1, 1024)
+
+        x = F.relu(self.bn4(self.fc1(x)))
+        x = F.relu(self.bn5(self.fc2(x)))
+        x = self.fc3(x)
+
+        iden = Variable(torch.from_numpy(np.eye(self.k).flatten().astype(np.float32))).view(1, self.k * self.k).repeat(
+            batchsize, 1)
+        if x.is_cuda:
+            iden = iden.cuda()
+        x = x + iden
+        x = x.view(-1, self.k, self.k)
+        return x
+
+
+class PointNetEncoder(nn.Module):
+    def __init__(self, global_feat=True, feature_transform=False, feature_dim=1024, channel=3):
+        super(PointNetEncoder, self).__init__()
+        self.stn = STN3d(channel)
+        self.conv1 = torch.nn.Conv1d(channel, 64, 1)
+        self.conv2 = torch.nn.Conv1d(64, 128, 1)
+        self.conv3 = torch.nn.Conv1d(128, feature_dim, 1)
+        self.bn1 = nn.BatchNorm1d(64)
+        self.bn2 = nn.BatchNorm1d(128)
+        self.bn3 = nn.BatchNorm1d(feature_dim)
+        self.feature_dim = feature_dim
+        self.global_feat = global_feat
+        self.feature_transform = feature_transform
+        if self.feature_transform:
+            self.fstn = STNkd(k=64)
+
+    def forward(self, x, x_mask=None):
+        B, D, N = x.size()
+        if x_mask is None:
+            x_mask = torch.ones(B, N, dtype=torch.bool, device=x.device)
+        if x_mask.dtype != torch.bool:
+            x_mask = x_mask > 0
+        trans = self.stn(x, x_mask)
+        x = x.transpose(2, 1)
+        if D > 3:
+            feature = x[:, :, 3:]
+            x = x[:, :, :3]
+        x = torch.bmm(x, trans)
+        if D > 3:
+            x = torch.cat([x, feature], dim=2)
+        x = x.transpose(2, 1)
+        x = F.relu(self.bn1(self.conv1(x)))
+
+        if self.feature_transform:
+            trans_feat = self.fstn(x, x_mask)
+            x = x.transpose(2, 1)
+            x = torch.bmm(x, trans_feat)
+            x = x.transpose(2, 1)
+        else:
+            trans_feat = None
+
+        pointfeat = x
+        x = F.relu(self.bn2(self.conv2(x)))
+        x = self.bn3(self.conv3(x))
+
+        if not self.global_feat:
+            x = x.view(-1, self.feature_dim, N)
+            return x, trans, trans_feat
+
+        x = torch.max(x, 2, keepdim=True)[0]
+        x = x.view(-1, self.feature_dim)
+        if self.global_feat:
+            return x, trans, trans_feat
+        else:
+            x = x.view(-1, self.feature_dim, 1).repeat(1, 1, N)
+            return torch.cat([x, pointfeat], 1), trans, trans_feat
+
diff --git a/src/pgnd/material/network/utils.py b/src/pgnd/material/network/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..72e438e81d35e18aeb48753d7a9660517b8524c8
--- /dev/null
+++ b/src/pgnd/material/network/utils.py
@@ -0,0 +1,41 @@
+import torch
+
+
+def get_grid_locations(x, num_grids_list, dx):
+    bsz = x.shape[0]
+    x_grid = torch.stack(torch.meshgrid(
+        torch.linspace(0, (num_grids_list[0] - 1) * dx, num_grids_list[0], device=x.device),
+        torch.linspace(0, (num_grids_list[1] - 1) * dx, num_grids_list[1], device=x.device),
+        torch.linspace(0, (num_grids_list[2] - 1) * dx, num_grids_list[2], device=x.device),
+    ), dim=-1).reshape(-1, 3)
+    grid_idxs = torch.stack(torch.meshgrid(
+        torch.linspace(0, (num_grids_list[0] - 1), num_grids_list[0], device=x.device),
+        torch.linspace(0, (num_grids_list[1] - 1), num_grids_list[1], device=x.device),
+        torch.linspace(0, (num_grids_list[2] - 1), num_grids_list[2], device=x.device),
+    ), dim=-1).reshape(-1, 3)
+
+    grid_hits = torch.zeros(bsz, num_grids_list[0], num_grids_list[1], num_grids_list[2], device=x.device)
+    for i in range(3):
+        for j in range(3):
+            for k in range(3):
+                grid_hits[:, \
+                    ((x[:, :, 0] / dx - 0.5).int() + i).clamp(0, num_grids_list[0] - 1), \
+                    ((x[:, :, 1] / dx - 0.5).int() + j).clamp(0, num_grids_list[1] - 1), \
+                    ((x[:, :, 2] / dx - 0.5).int() + k).clamp(0, num_grids_list[2] - 1)] = 1
+    grid_hits = grid_hits.sum(0) > 0
+    
+    x_grid = x_grid[grid_hits.reshape(-1)].reshape(1, -1, 3).repeat(bsz, 1, 1)
+    grid_idxs = grid_idxs[grid_hits.reshape(-1)]
+    return x_grid, grid_idxs
+
+
+def fill_grid_locations(feat, grid_idxs, num_grids_list):  
+    # feat: (bsz, num_active_grids, feature_dim)
+    # grid_idxs: (num_active_grids, 3)
+    bsz = feat.shape[0]
+    feat_filled = torch.zeros(bsz, num_grids_list[0], num_grids_list[1], num_grids_list[2], 3, device=feat.device)
+    grid_idxs_1d = grid_idxs[:, 0] * num_grids_list[1] * num_grids_list[2] + grid_idxs[:, 1] * num_grids_list[2] + grid_idxs[:, 2]
+    feat_filled = feat_filled.reshape(bsz, -1, 3)
+    feat_filled[:, grid_idxs_1d.long()] = feat.clone()
+    feat_filled = feat_filled.reshape(bsz, num_grids_list[0], num_grids_list[1], num_grids_list[2], 3)
+    return feat_filled
diff --git a/src/pgnd/material/pgnd.py b/src/pgnd/material/pgnd.py
new file mode 100644
index 0000000000000000000000000000000000000000..601be36e45e8b9fd1fb1b17c6e16cd9212661f76
--- /dev/null
+++ b/src/pgnd/material/pgnd.py
@@ -0,0 +1,145 @@
+from typing import Optional
+
+from omegaconf import DictConfig
+import numpy as np
+import torch
+import torch.nn as nn
+from torch import Tensor
+
+from .network.pointnet import PointNetEncoder
+from .network.nerf import CondNeRFModel
+from .network.utils import get_grid_locations, fill_grid_locations
+
+
+class PGNDModel(nn.Module):
+
+    def __init__(self, cfg: DictConfig) -> None:
+        super().__init__()
+
+        self.feature_dim = 64
+        self.radius = cfg.model.material.radius
+        self.n_history = cfg.sim.n_history
+        self.num_grids_list = cfg.sim.num_grids[:3]
+        self.dx = cfg.sim.num_grids[3]
+        self.inv_dx = 1 / self.dx
+        self.requires_grad = cfg.model.material.requires_grad
+        self.pe_num_func = int(np.log2(self.inv_dx)) + cfg.model.material.pe_num_func_res
+        self.pe_dim = 3 + self.pe_num_func * 6
+        self.output_scale = cfg.model.material.output_scale
+        self.input_scale = cfg.model.material.input_scale
+        self.absolute_y = cfg.model.material.absolute_y
+
+        self.encoder = PointNetEncoder(
+            global_feat=(cfg.model.material.radius <= 0), 
+            feature_transform=False, 
+            feature_dim=self.feature_dim, 
+            channel=6 * (1 + self.n_history),
+        )
+        self.decoder = CondNeRFModel(
+            xyz_dim=self.pe_dim,
+            condition_dim=self.feature_dim,
+            out_channel=3,
+            num_layers=2,
+            hidden_size=64,
+            skip_connect_every=4,
+        )
+
+    def positional_encoding(self, tensor):
+        num_encoding_functions = self.pe_num_func
+        if num_encoding_functions == 0:
+            assert include_input
+            return tensor
+
+        encoding = [tensor]  # include the input itself
+        frequency_bands = 2.0 ** torch.linspace(
+            0.0,
+            num_encoding_functions - 1,
+            num_encoding_functions,
+            dtype=tensor.dtype,
+            device=tensor.device,
+        )
+
+        for freq in frequency_bands:
+            for func in [torch.sin, torch.cos]:
+                encoding.append(func(tensor * freq))
+
+        # Special case, for no positional encoding
+        if len(encoding) == 1:
+            return encoding[0]
+        else:
+            return torch.cat(encoding, dim=-1)
+
+    def forward(self, x: Tensor, v: Tensor, x_his: Tensor, v_his: Tensor, enabled: Tensor) -> Tensor:
+        # x: (bsz, num_particles, 3)
+        # v: (bsz, num_particles, 3)
+        bsz = x.shape[0]
+        num_particles = x.shape[1]
+        v = v * self.input_scale
+        v_his = v_his * self.input_scale
+
+        x_his = x_his.reshape(bsz, num_particles, self.n_history, 3)
+        v_his = v_his.reshape(bsz, num_particles, self.n_history, 3)
+        x_his = x_his.detach()
+        v_his = v_his.detach()
+        
+        x_grid, grid_idxs = get_grid_locations(x, self.num_grids_list, self.dx)
+        x_grid = x_grid.detach()
+        grid_idxs = grid_idxs.detach()
+
+        # centering
+        x_center = x.mean(1, keepdim=True)
+        if self.absolute_y:
+            x_center[:, :, 1] = 0  # only centering x and z
+        x = x - x_center
+        x_his = x_his - x_center[:, :, None]
+        if self.training:
+            # random azimuth
+            theta = torch.rand(bsz, 1, device=x.device) * 2 * np.pi
+            rot = torch.stack([
+                torch.cos(theta), torch.zeros_like(theta), torch.sin(theta),
+                torch.zeros_like(theta), torch.ones_like(theta), torch.zeros_like(theta),
+                -torch.sin(theta), torch.zeros_like(theta), torch.cos(theta),
+            ], dim=-1).reshape(bsz, 3, 3)
+            inv_rot = rot.transpose(1, 2)
+        else:
+            rot = torch.eye(3, dtype=x.dtype, device=x.device).unsqueeze(0).repeat(bsz, 1, 1)
+            inv_rot = rot.transpose(1, 2)
+        x = torch.einsum('bij,bjk->bik', x, rot)
+        x_his = torch.einsum('bcij,bjk->bcik', x_his, rot)  # (bsz, num_particles, n_history, 3)
+        v = torch.einsum('bij,bjk->bik', v, rot)
+        v_his = torch.einsum('bcij,bjk->bcik', v_his, rot)  # (bsz, num_particles, n_history, 3)
+
+        x_grid = x_grid - x_center
+        x_grid = x_grid @ rot
+        x_his = x_his.reshape(bsz, num_particles, self.n_history * 3)
+        v_his = v_his.reshape(bsz, num_particles, self.n_history * 3)
+
+        feat = torch.cat([x, v, x_his, v_his], dim=-1)  # (bsz, num_particles, 6 * (1 + n_history))
+        feat = feat.permute(0, 2, 1)  # (bsz, 6 * (1 + n_history), num_particles)
+        feat, trans, trans_feat = self.encoder(feat, enabled)  # feat: (bsz, feature_dim, num_particles)
+
+        if self.radius > 0:
+            # aggregate neighborhood
+            # x.shape: (bsz, num_particles, 3)
+            # x_grid.shape: (bsz, num_grids_total, 3)
+            dist_pt_grid = torch.cdist(x_grid, x, p=2)
+            mask = dist_pt_grid < self.radius  # (bsz, num_grids_total, num_particles)
+            mask_normed = mask / (mask.sum(dim=-1, keepdim=True) + 1e-5)  # for each grid, normalize the weights
+            mask_normed = mask_normed.detach()
+            feat = mask_normed @ feat.permute(0, 2, 1)  # (bsz, num_grids_total, feature_dim)
+        else:
+            # global max pooling
+            feat = feat[:, None, :].repeat(1, x_grid.shape[1], 1)  # (bsz, num_grids_total, feature_dim)
+
+        # positional encoding and decoder
+        feat = feat.reshape(-1, self.feature_dim)
+        x_grid = x_grid.reshape(-1, 3)
+        x_grid = self.positional_encoding(x_grid)
+        feat = self.decoder(x_grid, feat)
+        feat = feat * self.output_scale
+        feat = feat.reshape(bsz, -1, feat.shape[-1])
+        feat = torch.bmm(feat, inv_rot)
+
+        # expand to full grid
+        feat = fill_grid_locations(feat, grid_idxs, self.num_grids_list)
+        return feat
diff --git a/src/pgnd/sim/__init__.py b/src/pgnd/sim/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..0ff8eccaae10b652ea2c4f96fa8b6f3d1cd43e2a
--- /dev/null
+++ b/src/pgnd/sim/__init__.py
@@ -0,0 +1,4 @@
+from .friction import *
+from .model import *
+from .sim import *
+from .utils import *
diff --git a/src/pgnd/sim/friction.py b/src/pgnd/sim/friction.py
new file mode 100644
index 0000000000000000000000000000000000000000..4e9f7faf43d810cbe4bacb104b4f93fd625571a0
--- /dev/null
+++ b/src/pgnd/sim/friction.py
@@ -0,0 +1,11 @@
+import torch
+import torch.nn as nn
+
+
+class Friction(nn.Module):
+    def __init__(self, mu_init=0.5):
+        super(Friction, self).__init__()
+        self.mu = torch.nn.Parameter(torch.tensor(mu_init))
+    
+    def clip(self):
+        self.mu.data = torch.clamp(self.mu.data, 0, 1)
diff --git a/src/pgnd/sim/model.py b/src/pgnd/sim/model.py
new file mode 100644
index 0000000000000000000000000000000000000000..99a95b8a2579fd18ec795a992fb92c574cfbf2d7
--- /dev/null
+++ b/src/pgnd/sim/model.py
@@ -0,0 +1,185 @@
+from typing import Optional, Union, Sequence, Any
+from omegaconf import DictConfig
+import numpy as np
+import torch
+from torch import Tensor
+import warp as wp
+
+from .utils import ConstantBatch, StaticsBatch, CollidersBatch, GridDataBatch, ParticleDataBatch
+
+
+class SimModelBatch:
+
+    def __init__(self, constant: ConstantBatch, device: wp.context.Devicelike = None, batch_size: int = 1, requires_grad: bool = False) -> None:
+        self.constant = constant
+        self.device = wp.get_device(device)
+        self.requires_grad = requires_grad
+        self.batch_size = batch_size
+
+    def update_friction(self, friction) -> None:
+        self.constant.update_friction(friction, self.requires_grad)
+
+    @staticmethod
+    @wp.kernel
+    def grid_op_batch(
+            constant: ConstantBatch,
+            colliders: CollidersBatch,
+            grid_curr: GridDataBatch,
+            grid_next: GridDataBatch,
+        ) -> None:
+
+        b, px, py, pz = wp.tid()
+
+        v = grid_curr.v[b, px, py, pz]
+
+        friction = constant.friction[b, 0]
+
+        if px < constant.bound and v[0] < 0.0:
+            impulse = wp.vec3(v[0], 0.0, 0.0)
+            v = wp.vec3(0.0, v[1], v[2])
+            friction_impulse = wp.float32(-1.) * friction * wp.length(impulse) * wp.normalize(v)
+            friction_impulse *= wp.min(1.0, wp.length(v) / (wp.length(friction_impulse) + 1e-10))
+            v = v + friction_impulse
+        if py < constant.bound and v[1] < 0.0:
+            impulse = wp.vec3(0.0, v[1], 0.0)
+            v = wp.vec3(v[0], 0.0, v[2])
+            friction_impulse = wp.float32(-1.) * friction * wp.length(impulse) * wp.normalize(v)
+            friction_impulse *= wp.min(1.0, wp.length(v) / (wp.length(friction_impulse) + 1e-10))
+            v = v + friction_impulse
+        if pz < constant.bound and v[2] < 0.0:
+            impulse = wp.vec3(0.0, 0.0, v[2])
+            v = wp.vec3(v[0], v[1], 0.0)
+            friction_impulse = wp.float32(-1.) * friction * wp.length(impulse) * wp.normalize(v)
+            friction_impulse *= wp.min(1.0, wp.length(v) / (wp.length(friction_impulse) + 1e-10))
+            v = v + friction_impulse
+        if px > int(constant.num_grids_list[0]) - constant.bound and v[0] > 0.0:
+            impulse = wp.vec3(v[0], 0.0, 0.0)
+            v = wp.vec3(0.0, v[1], v[2])
+            friction_impulse = wp.float32(-1.) * friction * wp.length(impulse) * wp.normalize(v)
+            friction_impulse *= wp.min(1.0, wp.length(v) / (wp.length(friction_impulse) + 1e-10))
+            v = v + friction_impulse
+        if py > int(constant.num_grids_list[1]) - constant.bound and v[1] > 0.0:
+            impulse = wp.vec3(0.0, v[1], 0.0)
+            v = wp.vec3(v[0], 0.0, v[2])
+            friction_impulse = wp.float32(-1.) * friction * wp.length(impulse) * wp.normalize(v)
+            friction_impulse *= wp.min(1.0, wp.length(v) / (wp.length(friction_impulse) + 1e-10))
+            v = v + friction_impulse
+        if pz > int(constant.num_grids_list[2]) - constant.bound and v[2] > 0.0:
+            impulse = wp.vec3(0.0, 0.0, v[2])
+            v = wp.vec3(v[0], v[1], 0.0)
+            friction_impulse = wp.float32(-1.) * friction * wp.length(impulse) * wp.normalize(v)
+            friction_impulse *= wp.min(1.0, wp.length(v) / (wp.length(friction_impulse) + 1e-10))
+            v = v + friction_impulse
+
+        for gripper_id in range(colliders.gripper_centers.shape[1]):
+
+            dx = wp.float32(px) * constant.dx - colliders.gripper_centers[b, gripper_id][0]
+            dy = wp.float32(py) * constant.dx - colliders.gripper_centers[b, gripper_id][1]
+            dz = wp.float32(pz) * constant.dx - colliders.gripper_centers[b, gripper_id][2]
+
+            grid_from_gripper = wp.vec3(dx, dy, dz)
+            grid_from_gripper_norm = wp.length(grid_from_gripper)
+
+            gripper_add_radii = 0.0
+            if grid_from_gripper_norm < colliders.gripper_radii[b, gripper_id] + gripper_add_radii and colliders.gripper_open[b, gripper_id] < 0.5:
+                if wp.length(colliders.gripper_quat[b, gripper_id]) > 0:  # filter out
+                    gripper_vel = colliders.gripper_vels[b, gripper_id]
+                    gripper_quat_vel = colliders.gripper_quat_vel[b, gripper_id]
+
+                    gripper_angular_vel = wp.length(gripper_quat_vel)
+                    gripper_quat_axis = gripper_quat_vel / (gripper_angular_vel + 1e-10)
+
+                    grid_from_gripper_axis = grid_from_gripper - \
+                        wp.dot(gripper_quat_axis, grid_from_gripper) * gripper_quat_axis
+                    v = wp.cross(gripper_quat_vel, grid_from_gripper_axis) + gripper_vel
+
+                else:
+                    v = colliders.gripper_vels[b, gripper_id]
+
+        grid_next.v[b, px, py, pz] = v
+
+    @staticmethod
+    @wp.kernel
+    def g2p_batch(
+            constant: ConstantBatch,
+            statics: StaticsBatch,
+            particle_curr: ParticleDataBatch,
+            grid_next: GridDataBatch,
+            particle_next: ParticleDataBatch,
+        ) -> None:
+
+        b, p = wp.tid()
+
+        if statics.enabled[b, p] == 0:
+            return
+
+        p_x = particle_curr.x[b, p] * constant.inv_dx
+        base_x = int(p_x[0] - 0.5)
+        base_y = int(p_x[1] - 0.5)
+        base_z = int(p_x[2] - 0.5)
+        f_x = p_x - wp.vec3(
+            float(base_x),
+            float(base_y),
+            float(base_z))
+
+        # quadratic kernels  [http://mpm.graphics   Eqn. 123, with x=fx,fx-1,fx-2]
+        wa = wp.vec3(1.5) - f_x
+        wb = f_x - wp.vec3(1.0)
+        wc = f_x - wp.vec3(0.5)
+
+        w = wp.mat33(
+            wp.cw_mul(wa, wa) * 0.5,
+            wp.vec3(0.75) - wp.cw_mul(wb, wb),
+            wp.cw_mul(wc, wc) * 0.5,
+        )
+
+        new_v = wp.vec3(0.0)
+
+        for i in range(3):
+            for j in range(3):
+                for k in range(3):
+                    weight = w[0, i] * w[1, j] * w[2, k]
+
+                    v = grid_next.v[b, base_x + i, base_y + j, base_z + k]
+                    new_v = new_v + weight * v
+
+        particle_next.v[b, p] = new_v
+
+        bound = statics.clip_bound[b, p] * constant.dx + constant.eps
+        new_x = particle_curr.x[b, p] + constant.dt * new_v
+        new_x = wp.vec3(
+            wp.clamp(new_x[0], 0.0 + bound, float(constant.num_grids_list[0]) * constant.dx - bound),
+            wp.clamp(new_x[1], 0.0 + bound, float(constant.num_grids_list[1]) * constant.dx - bound),
+            wp.clamp(new_x[2], 0.0 + bound, float(constant.num_grids_list[2]) * constant.dx - bound),
+        )
+        particle_next.x[b, p] = new_x
+
+
+def build_model(
+        cfg: DictConfig,
+        batch_size: int,
+        device: wp.context.Devicelike = None,
+        requires_grad: bool = False
+    ) -> SimModelBatch:
+
+    dt: float = eval(cfg.sim.dt) if isinstance(cfg.sim.dt, str) else cfg.sim.dt
+    bound: int = cfg.sim.bound
+    eps: float = cfg.sim.eps
+    assert len(cfg.sim.num_grids) == 4
+    num_grids_list: list = cfg.sim.num_grids[:3]
+    dx: float = cfg.sim.num_grids[3]
+    inv_dx: float = 1 / dx
+
+    constant = ConstantBatch()
+    constant.init()
+    constant.dt = dt
+    constant.bound = bound
+    constant.dx = dx
+    constant.inv_dx = inv_dx
+    constant.eps = eps
+    friction = np.array([cfg.model.friction.value], dtype=np.float32)[None].repeat(batch_size, axis=0)
+    constant.friction = wp.from_numpy(friction, dtype=float)  # [bsz, 1]
+    constant.num_grids_list = wp.vec3(*num_grids_list)
+
+    model = SimModelBatch(constant, device, batch_size, requires_grad)
+    return model
diff --git a/src/pgnd/sim/sim.py b/src/pgnd/sim/sim.py
new file mode 100644
index 0000000000000000000000000000000000000000..156901a05319db0db76952c79167c419eb3e2262
--- /dev/null
+++ b/src/pgnd/sim/sim.py
@@ -0,0 +1,136 @@
+from typing import Optional
+from omegaconf import DictConfig
+import torch
+import torch.autograd as autograd
+import torch.nn as nn
+from torch import Tensor
+import warp as wp
+
+from .utils import StaticsBatch, CollidersBatch, ParticleDataBatch, GridDataBatch
+from .model import SimModelBatch, build_model
+from pgnd.utils import Tape, CondTape
+
+
+class SimFunctionWithFrictionBatch(autograd.Function):
+
+    @staticmethod
+    def forward(
+            ctx: autograd.function.FunctionCtx,
+            model: SimModelBatch,
+            statics: StaticsBatch,
+            colliders: CollidersBatch,
+            particles_curr: ParticleDataBatch,
+            particles_next: ParticleDataBatch,
+            grid_curr: GridDataBatch,
+            grid_next: GridDataBatch,
+            friction: Tensor,
+            x: Tensor,
+            v: Tensor,
+            pred: Tensor) -> tuple[Tensor, Tensor]:
+
+        tape: Tape = Tape()
+        model.update_friction(friction)
+        particles_curr.from_torch(x=x, v=v)
+        grid_curr.clear()
+        grid_curr.zero_grad()
+        grid_curr.from_torch(v=pred)
+        grid_next.clear()
+        grid_next.zero_grad()
+
+        device = model.device
+        constant = model.constant
+        batch_size = model.batch_size
+
+        num_grids_list = [int(constant.num_grids_list[0]), int(constant.num_grids_list[1]), int(constant.num_grids_list[2])]
+        num_particles = particles_curr.x.shape[1]
+
+        with CondTape(tape, model.requires_grad):
+            wp.launch(model.grid_op_batch, 
+                dim=[batch_size] + [num_grids_list[0], num_grids_list[1], num_grids_list[2]],
+                inputs=[constant, colliders, grid_curr], 
+                outputs=[grid_next],
+                device=device
+            )
+            wp.launch(model.g2p_batch, 
+                dim=[batch_size, num_particles], 
+                inputs=[constant, statics, particles_curr, grid_next], 
+                outputs=[particles_next],
+                device=device
+            )
+
+        x_next, v_next = particles_next.to_torch()
+
+        ctx.tape = tape
+        ctx.particles_curr = particles_curr
+        ctx.particles_next = particles_next
+        ctx.grid_curr = grid_curr
+
+        return x_next, v_next
+
+    @staticmethod
+    def backward(
+            ctx: autograd.function.FunctionCtx,
+            grad_x_next: Tensor,
+            grad_v_next: Tensor,
+            ) -> tuple[None, None, None, None, None, None, None, None, Tensor, Tensor, Tensor]:
+
+        tape: Tape = ctx.tape
+        particles_curr: ParticleDataBatch = ctx.particles_curr
+        particles_next: ParticleDataBatch = ctx.particles_next
+        grid_curr: GridDataBatch = ctx.grid_curr
+
+        if torch.isnan(grad_x_next).any() or torch.isnan(grad_v_next).any():
+            import ipdb; ipdb.set_trace()
+
+        tape.backward(
+            grads={
+                particles_next.x: wp.from_torch(grad_x_next.contiguous(), dtype=wp.vec3),
+                particles_next.v: wp.from_torch(grad_v_next.contiguous(), dtype=wp.vec3),
+            }
+        )
+
+        grad_x, grad_v = particles_curr.to_torch_grad()
+        grad_pred = grid_curr.to_torch_grad()
+
+        if grad_x is not None:
+            torch.nan_to_num_(grad_x, 0.0, 0.0, 0.0)
+        if grad_v is not None:
+            torch.nan_to_num_(grad_v, 0.0, 0.0, 0.0)
+        if grad_pred is not None:
+            torch.nan_to_num_(grad_pred, 0.0, 0.0, 0.0)
+
+        return None, None, None, None, None, None, None, None, grad_x, grad_v, grad_pred
+
+
+class CacheDiffSimWithFrictionBatch(nn.Module):
+
+    def __init__(self, cfg: DictConfig, num_steps: int, batch_size: int, device: wp.context.Devicelike = None, requires_grad: bool = False) -> None:
+        super().__init__()
+        self.model = build_model(cfg=cfg, batch_size=batch_size, device=device, requires_grad=requires_grad)
+        self.curr_particles = [None for _ in range(num_steps)]
+        self.next_particles = [None for _ in range(num_steps)]
+        self.curr_grids = [None for _ in range(num_steps)]
+        self.next_grids = [None for _ in range(num_steps)]
+
+    def forward(self, statics: StaticsBatch, colliders: CollidersBatch, step: int, x: Tensor, v: Tensor, friction: Tensor, pred: Tensor) -> tuple[Tensor, Tensor]:
+        shape = (x.size(0), x.size(1))
+        if self.curr_particles[step] is None:
+            self.curr_particles[step] = ParticleDataBatch()
+            self.curr_particles[step].init(shape, self.model.device, self.model.requires_grad)
+        if self.next_particles[step] is None:
+            self.next_particles[step] = ParticleDataBatch()
+            self.next_particles[step].init(shape, self.model.device, self.model.requires_grad)
+        particles_curr = self.curr_particles[step]
+        particles_next = self.next_particles[step]
+
+        shape = (pred.size(0), pred.size(1), pred.size(2), pred.size(3))
+        if self.curr_grids[step] is None:
+            self.curr_grids[step] = GridDataBatch()
+            self.curr_grids[step].init(shape, self.model.device, self.model.requires_grad)
+        grid_curr = self.curr_grids[step]
+        if self.next_grids[step] is None:
+            self.next_grids[step] = GridDataBatch()
+            self.next_grids[step].init(shape, self.model.device, self.model.requires_grad)
+        grid_next = self.next_grids[step]
+
+        return SimFunctionWithFrictionBatch.apply(self.model, statics, colliders, particles_curr, particles_next, grid_curr, grid_next, friction, x, v, pred)
diff --git a/src/pgnd/sim/utils.py b/src/pgnd/sim/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..2cbfebf95acfb400f7f2906ec28e8d8f52e959fb
--- /dev/null
+++ b/src/pgnd/sim/utils.py
@@ -0,0 +1,253 @@
+from typing import Optional, Union, Sequence, Any
+
+import numpy as np
+import torch
+from torch import Tensor
+import warp as wp
+
+
+@wp.struct
+class GridDataBatch(object):
+
+    batch_size: int
+    v: wp.array(dtype=wp.vec3, ndim=4)
+
+    def init(self, shape: Sequence[int], device: wp.context.Devicelike = None, requires_grad: bool = False) -> None:
+        self.batch_size = shape[0]
+        self.v = wp.zeros(shape=shape, dtype=wp.vec3, ndim=4, device=device, requires_grad=requires_grad)
+
+    def clear(self) -> None:
+        self.v.zero_()
+
+    def zero_grad(self) -> None:
+        if self.v.requires_grad:
+            self.v.grad.zero_()
+    
+    def from_torch(self, v: Optional[Tensor] = None) -> None:
+        if v is not None:
+            self.v = wp.from_torch(v.contiguous(), dtype=wp.vec3, requires_grad=self.v.requires_grad)
+        
+    def to_torch(self) -> Tensor:
+        v = wp.to_torch(self.v).requires_grad_(self.v.requires_grad)
+        return v
+    
+    def to_torch_grad(self) -> Tensor:
+        grad_v = wp.to_torch(self.v.grad) if self.v.grad is not None else None
+        return grad_v
+    
+    def from_torch_grad(self, grad_v: Optional[Tensor] = None) -> None:
+        if grad_v is not None:
+            self.v.grad = wp.from_torch(grad_v.contiguous(), dtype=wp.vec3)
+
+
+@wp.struct
+class ParticleDataBatch(object):
+
+    x: wp.array(dtype=wp.vec3, ndim=2)
+    v: wp.array(dtype=wp.vec3, ndim=2)
+
+    def init(self, shape: Union[Sequence[int], int], device: wp.context.Devicelike = None, requires_grad: bool = False) -> None:
+        self.x = wp.zeros(shape=shape, dtype=wp.vec3, device=device, requires_grad=requires_grad)
+        self.v = wp.zeros(shape=shape, dtype=wp.vec3, device=device, requires_grad=requires_grad)
+
+    def clear(self) -> None:
+        self.x.zero_()
+        self.v.zero_()
+    
+    def zero_grad(self) -> None:
+        if self.x.requires_grad:
+            self.x.grad.zero_()
+        if self.v.requires_grad:
+            self.v.grad.zero_()
+
+    def from_torch(self, x: Optional[Tensor] = None, v: Optional[Tensor] = None) -> None:
+        if x is not None:
+            self.x = wp.from_torch(x.contiguous(), dtype=wp.vec3, requires_grad=self.x.requires_grad)
+        if v is not None:
+            self.v = wp.from_torch(v.contiguous(), dtype=wp.vec3, requires_grad=self.v.requires_grad)
+
+    def to_torch(self) -> tuple[Tensor, Tensor]:
+        x = wp.to_torch(self.x).requires_grad_(self.x.requires_grad)
+        v = wp.to_torch(self.v).requires_grad_(self.v.requires_grad)
+        return x, v
+
+    def from_torch_grad(self, grad_x: Optional[Tensor] = None, grad_v: Optional[Tensor] = None) -> None:
+        if grad_x is not None:
+            self.x.grad = wp.from_torch(grad_x.contiguous(), dtype=wp.vec3)
+        if grad_v is not None:
+            self.v.grad = wp.from_torch(grad_v.contiguous(), dtype=wp.vec3)
+
+    def to_torch_grad(self) -> tuple[Optional[Tensor], Optional[Tensor]]:
+        grad_x = wp.to_torch(self.x.grad) if self.x.grad is not None else None
+        grad_v = wp.to_torch(self.v.grad) if self.v.grad is not None else None
+        return grad_x, grad_v
+
+
+@wp.struct
+class StaticsBatch(object):
+
+    clip_bound: wp.array(dtype=float, ndim=2)
+    enabled: wp.array(dtype=int, ndim=2)
+
+    def init(self, shape: Union[Sequence[int], int], device: wp.context.Devicelike = None) -> None:
+        self.clip_bound = wp.zeros(shape=(shape[0], shape[1]), dtype=float, device=device, requires_grad=False)
+        self.enabled = wp.zeros(shape=(shape[0], shape[1]), dtype=int, device=device, requires_grad=False)
+
+    @staticmethod
+    @wp.kernel
+    def set_float(x: wp.array(dtype=float, ndim=2), value: wp.array(dtype=float)) -> None:
+        b, p = wp.tid()
+        x[b, p] = value[b]
+
+    @staticmethod
+    @wp.kernel
+    def set_int_per_particle(x: wp.array(dtype=int, ndim=2), value: wp.array(dtype=int, ndim=2)) -> None:
+        b, p = wp.tid()
+        x[b, p] = value[b, p]
+
+    def update_clip_bound(self, clip_bound: Tensor) -> None:
+        clip_bound = wp.from_numpy(clip_bound.numpy(), dtype=float)
+        wp.launch(self.set_float, dim=self.clip_bound.shape, inputs=[self.clip_bound, clip_bound], device=self.clip_bound.device)
+
+    def update_enabled(self, enabled: Tensor) -> None:
+        enabled = wp.from_numpy(enabled.numpy(), dtype=int)
+        wp.launch(self.set_int_per_particle, dim=self.enabled.shape, inputs=[self.enabled, enabled], device=self.enabled.device)
+
+
+@wp.struct
+class CollidersBatch(object):
+
+    gripper_centers: wp.array(dtype=wp.vec3, ndim=2)
+    gripper_vels: wp.array(dtype=wp.vec3, ndim=2)
+    gripper_radii: wp.array(dtype=float, ndim=2)
+    gripper_quat: wp.array(dtype=wp.quat, ndim=2)
+    gripper_quat_vel: wp.array(dtype=wp.vec3, ndim=2)
+    gripper_open: wp.array(dtype=float, ndim=2)
+
+    def init(self, shape: Sequence[int], device: wp.context.Devicelike = None, use_quat: bool = False) -> None:
+        self.gripper_centers = wp.zeros(shape=shape, dtype=wp.vec3, device=device, requires_grad=False)
+        self.gripper_vels = wp.zeros(shape=shape, dtype=wp.vec3, device=device, requires_grad=False)
+        self.gripper_radii = wp.zeros(shape=shape, dtype=float, device=device, requires_grad=False)
+        self.gripper_quat = wp.zeros(shape=shape, dtype=wp.quat, device=device, requires_grad=False)
+        self.gripper_quat_vel = wp.zeros(shape=shape, dtype=wp.vec3, device=device, requires_grad=False)
+        self.gripper_open = wp.zeros(shape=shape, dtype=float, device=device, requires_grad=False)
+
+    @staticmethod
+    @wp.kernel
+    def set_int(x: wp.array(dtype=wp.int32, ndim=2), value: wp.array(dtype=wp.int32, ndim=2)) -> None:
+        b, p = wp.tid()
+        x[b, p] = value[b, p]
+
+    @staticmethod
+    @wp.kernel
+    def set_float(x: wp.array(dtype=wp.float32, ndim=2), value: wp.array(dtype=wp.float32, ndim=2)) -> None:
+        b, p = wp.tid()
+        x[b, p] = value[b, p]
+    
+    @staticmethod
+    @wp.kernel
+    def set_vec3(x: wp.array(dtype=wp.vec3, ndim=2), value: wp.array(dtype=wp.vec3, ndim=2)) -> None:
+        b, p = wp.tid()
+        x[b, p] = value[b, p]
+
+    @staticmethod
+    @wp.kernel
+    def add_vec3(x: wp.array(dtype=wp.vec3, ndim=2), value: wp.array(dtype=wp.vec3, ndim=2)) -> None:
+        b, p = wp.tid()
+        wp.atomic_add(x, b, p, value[b, p])
+    
+    @staticmethod
+    @wp.kernel
+    def set_quat(x: wp.array(dtype=wp.quat, ndim=2), value: wp.array(dtype=wp.quat, ndim=2)) -> None:
+        b, p = wp.tid()
+        x[b, p] = value[b, p]
+    
+    def update_grippers(self, gripper: Tensor) -> None:
+        assert gripper.shape[-1] == 15  # quat, quat_vel (axis-angle)
+        center, vel, quat, quat_vel, _, openness = torch.split(gripper, [3, 3, 4, 3, 1, 1], dim=-1)
+
+        center_wp = wp.from_torch(center.contiguous(), dtype=wp.vec3)
+        wp.launch(self.set_vec3, dim=self.gripper_centers.shape, inputs=[self.gripper_centers, center_wp], device=self.gripper_centers.device)
+
+        vel_wp = wp.from_torch(vel.contiguous(), dtype=wp.vec3)
+        wp.launch(self.set_vec3, dim=self.gripper_vels.shape, inputs=[self.gripper_vels, vel_wp], device=self.gripper_vels.device)
+
+        quat_wp = wp.from_torch(quat.contiguous(), dtype=wp.quat)
+        wp.launch(self.set_quat, dim=self.gripper_quat.shape, inputs=[self.gripper_quat, quat_wp], device=self.gripper_quat.device)
+
+        quat_vel_wp = wp.from_torch(quat_vel.contiguous(), dtype=wp.vec3)
+        wp.launch(self.set_vec3, dim=self.gripper_quat_vel.shape, inputs=[self.gripper_quat_vel, quat_vel_wp], device=self.gripper_quat_vel.device)
+
+        openness_wp = wp.from_torch(openness.squeeze(-1).contiguous(), dtype=wp.float32)
+        wp.launch(self.set_float, dim=self.gripper_open.shape, inputs=[self.gripper_open, openness_wp], device=self.gripper_open.device)
+
+    def initialize_grippers(self, gripper: Tensor) -> None:
+        assert gripper.shape[-1] == 15  # quat, quat_vel (axis-angle)
+        center, vel, quat, quat_vel, radius, openness = torch.split(gripper, [3, 3, 4, 3, 1, 1], dim=-1)
+
+        center_wp = wp.from_torch(center.contiguous(), dtype=wp.vec3)
+        wp.launch(self.set_vec3, dim=self.gripper_centers.shape, inputs=[self.gripper_centers, center_wp], device=self.gripper_centers.device)
+
+        vel_wp = wp.from_torch(vel.contiguous(), dtype=wp.vec3)
+        wp.launch(self.set_vec3, dim=self.gripper_vels.shape, inputs=[self.gripper_vels, vel_wp], device=self.gripper_vels.device)
+
+        quat_wp = wp.from_torch(quat.contiguous(), dtype=wp.quat)
+        wp.launch(self.set_quat, dim=self.gripper_quat.shape, inputs=[self.gripper_quat, quat_wp], device=self.gripper_quat.device)
+
+        quat_vel_wp = wp.from_torch(quat_vel.contiguous(), dtype=wp.vec3)
+        wp.launch(self.set_vec3, dim=self.gripper_quat_vel.shape, inputs=[self.gripper_quat_vel, quat_vel_wp], device=self.gripper_quat_vel.device)
+
+        radius_wp = wp.from_torch(radius.squeeze(-1).contiguous(), dtype=wp.float32)
+        wp.launch(self.set_float, dim=self.gripper_radii.shape, inputs=[self.gripper_radii, radius_wp], device=self.gripper_radii.device)
+
+        openness_wp = wp.from_torch(openness.squeeze(-1).contiguous(), dtype=wp.float32)
+        wp.launch(self.set_float, dim=self.gripper_open.shape, inputs=[self.gripper_open, openness_wp], device=self.gripper_open.device)
+
+    def export(self):
+        data = {}
+        if self.gripper_centers.shape[1] > 0:
+            gripper_centers = self.gripper_centers.numpy()
+            grippers = np.zeros((gripper_centers.shape[0], gripper_centers.shape[1], 15))
+            grippers[:, :, :3] = gripper_centers
+            grippers[:, :, 3:6] = self.gripper_vels.numpy()
+            grippers[:, :, 6:10] = self.gripper_quat.numpy()
+            grippers[:, :, 10:13] = self.gripper_quat_vel.numpy()
+            grippers[:, :, 13] = self.gripper_radii.numpy()
+            grippers[:, :, 14] = self.gripper_open.numpy()
+            data['grippers'] = grippers
+        return data
+
+
+@wp.struct
+class ConstantBatch(object):
+
+    num_grids_list: wp.vec3
+    dt: float
+    bound: int
+    gravity: wp.vec3
+    dx: float
+    inv_dx: float
+    eps: float
+    friction: wp.array(dtype=wp.float32, ndim=2)
+
+    def init(self):
+        # self.num_grids = 0
+        self.num_grids_list = wp.vec3(0, 0, 0)
+        self.dt = 0.0
+        self.bound = 0
+        self.gravity = wp.vec3(0.0, 0.0, 0.0)
+        self.dx = 0.0
+        self.inv_dx = 0.0
+        self.eps = 0.0
+        self.friction = wp.zeros(shape=(1, 1), dtype=wp.float32)
+    
+    def update_friction(self, friction, requires_grad=False) -> None:
+        self.friction = wp.from_torch(friction[..., None].to(torch.float32), dtype=wp.float32)
+        self.friction.requires_grad = requires_grad
+    
+    def zero_grad(self) -> None:
+        if self.friction.requires_grad:
+            self.friction.grad.zero_()
+    
+    def to_torch_grad(self) -> Optional[Tensor]:
+        return wp.to_torch(self.friction.grad) if self.friction.grad is not None else None
diff --git a/src/pgnd/utils.py b/src/pgnd/utils.py
new file mode 100644
index 0000000000000000000000000000000000000000..0ce9d4e954688c06bf307eda9affdada344c2270
--- /dev/null
+++ b/src/pgnd/utils.py
@@ -0,0 +1,90 @@
+from typing import Union, Dict, Optional
+from pathlib import Path
+from omegaconf import DictConfig
+import sys
+import shutil
+import numpy as np
+import wandb
+import warp as wp
+
+
+Tape = wp.Tape
+
+class CondTape(object):
+    def __init__(self, tape: Optional[Tape], cond: bool = True) -> None:
+        self.tape = tape
+        self.cond = cond
+
+    def __enter__(self):
+        if self.tape is not None and self.cond:
+            self.tape.__enter__()
+
+    def __exit__(self, exc_type, exc_value, traceback):
+        if self.tape is not None and self.cond:
+            self.tape.__exit__(exc_type, exc_value, traceback)
+
+
+def cfg2dict(cfg: DictConfig) -> Dict:
+    """
+    Recursively convert OmegaConf to vanilla dict
+    :param cfg:
+    :return:
+    """
+    cfg_dict = {}
+    for k, v in cfg.items():
+        if type(v) == DictConfig:
+            cfg_dict[k] = cfg2dict(v)
+        else:
+            cfg_dict[k] = v
+    return cfg_dict
+
+
+class Logger:
+
+    def __init__(self, cfg, project='pgnd-train', entity='kaiz'):
+        wandb.init(project=project, entity=entity, name=cfg.train.name)
+        wandb.config = cfg2dict(cfg)
+    
+    def add_scalar(self, tag, scalar, step=None):
+        wandb.log({tag: scalar}, step=step)
+
+    def add_image(self, tag, img, step=None, scale=True):
+        if scale:
+            img = (img - img.min()) / (img.max() - img.min())
+        wandb.log({tag: wandb.Image(img)}, step=step)
+
+    def add_video(self, tag, video, step=None):
+        wandb.log({tag: wandb.Video(video)}, step=step)
+
+
+def mkdir(path: Path, resume=False, overwrite=False) -> None:
+    while True:
+        if overwrite:
+            if path.is_dir():
+                print('overwriting directory ({})'.format(path))
+            shutil.rmtree(path, ignore_errors=True)
+            path.mkdir(parents=True, exist_ok=True)
+            return
+        elif resume:
+            print('resuming directory ({})'.format(path))
+            path.mkdir(parents=True, exist_ok=True)
+            return
+        else:
+            if path.exists():
+                feedback = input('target directory ({}) already exists, overwrite? [Y/r/n] '.format(path))
+                ret = feedback.casefold()
+            else:
+                ret = 'y'
+            if ret == 'n':
+                sys.exit(0)
+            elif ret == 'r':
+                resume = True
+            elif ret == 'y':
+                overwrite = True
+
+
+def get_root(path: Union[str, Path], name: str = '.root') -> Path:
+    root = Path(path).resolve()
+    while not (root / name).is_file():
+        root = root.parent
+    return root
diff --git a/src/third-party/diff-gaussian-rasterization-w-depth/.gitignore b/src/third-party/diff-gaussian-rasterization-w-depth/.gitignore
new file mode 100644
index 0000000000000000000000000000000000000000..2eec4f76ad696c1e89917f874aa69bdc5bb87e69
--- /dev/null
+++ b/src/third-party/diff-gaussian-rasterization-w-depth/.gitignore
@@ -0,0 +1,5 @@
+build/
+diff_gaussian_rasterization.egg-info/
+dist/
+.idea/
+third_party/
\ No newline at end of file
diff --git a/src/third-party/diff-gaussian-rasterization-w-depth/.gitmodules b/src/third-party/diff-gaussian-rasterization-w-depth/.gitmodules
new file mode 100644
index 0000000000000000000000000000000000000000..4553c29f4224a9a8723482bc9aca759a97693a64
--- /dev/null
+++ b/src/third-party/diff-gaussian-rasterization-w-depth/.gitmodules
@@ -0,0 +1,3 @@
+[submodule "third_party/glm"]
+	path = third_party/glm
+	url = https://github.com/g-truc/glm.git
diff --git a/src/third-party/diff-gaussian-rasterization-w-depth/CMakeLists.txt b/src/third-party/diff-gaussian-rasterization-w-depth/CMakeLists.txt
new file mode 100644
index 0000000000000000000000000000000000000000..9fdb9cbfdcc0fc2b81595cd616912067291ea0b1
--- /dev/null
+++ b/src/third-party/diff-gaussian-rasterization-w-depth/CMakeLists.txt
@@ -0,0 +1,36 @@
+#
+# Copyright (C) 2023, Inria
+# GRAPHDECO research group, https://team.inria.fr/graphdeco
+# All rights reserved.
+#
+# This software is free for non-commercial, research and evaluation use 
+# under the terms of the LICENSE.md file.
+#
+# For inquiries contact  george.drettakis@inria.fr
+#
+
+cmake_minimum_required(VERSION 3.20)
+
+project(DiffRast LANGUAGES CUDA CXX)
+
+set(CMAKE_CXX_STANDARD 17)
+set(CMAKE_CXX_EXTENSIONS OFF)
+set(CMAKE_CUDA_STANDARD 17)
+
+set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS}")
+
+add_library(CudaRasterizer
+	cuda_rasterizer/backward.h
+	cuda_rasterizer/backward.cu
+	cuda_rasterizer/forward.h
+	cuda_rasterizer/forward.cu
+	cuda_rasterizer/auxiliary.h
+	cuda_rasterizer/rasterizer_impl.cu
+	cuda_rasterizer/rasterizer_impl.h
+	cuda_rasterizer/rasterizer.h
+)
+
+set_target_properties(CudaRasterizer PROPERTIES CUDA_ARCHITECTURES "75;86")
+
+target_include_directories(CudaRasterizer PUBLIC ${CMAKE_CURRENT_SOURCE_DIR}/cuda_rasterizer)
+target_include_directories(CudaRasterizer PRIVATE third_party/glm ${CMAKE_CUDA_TOOLKIT_INCLUDE_DIRECTORIES})
diff --git a/src/third-party/diff-gaussian-rasterization-w-depth/LICENSE.md b/src/third-party/diff-gaussian-rasterization-w-depth/LICENSE.md
new file mode 100644
index 0000000000000000000000000000000000000000..e948bb6d789a112ba2c73d4cf7907d503ad86198
--- /dev/null
+++ b/src/third-party/diff-gaussian-rasterization-w-depth/LICENSE.md
@@ -0,0 +1,83 @@
+Gaussian-Splatting License  
+===========================  
+
+**Inria** and **the Max Planck Institut for Informatik (MPII)** hold all the ownership rights on the *Software* named **gaussian-splatting**.  
+The *Software* is in the process of being registered with the Agence pour la Protection des  
+Programmes (APP).  
+
+The *Software* is still being developed by the *Licensor*.  
+
+*Licensor*'s goal is to allow the research community to use, test and evaluate  
+the *Software*.  
+
+## 1.  Definitions  
+
+*Licensee* means any person or entity that uses the *Software* and distributes  
+its *Work*.  
+
+*Licensor* means the owners of the *Software*, i.e Inria and MPII  
+
+*Software* means the original work of authorship made available under this  
+License ie gaussian-splatting.  
+
+*Work* means the *Software* and any additions to or derivative works of the  
+*Software* that are made available under this License.  
+
+
+## 2.  Purpose  
+This license is intended to define the rights granted to the *Licensee* by  
+Licensors under the *Software*.  
+
+## 3.  Rights granted  
+
+For the above reasons Licensors have decided to distribute the *Software*.  
+Licensors grant non-exclusive rights to use the *Software* for research purposes  
+to research users (both academic and industrial), free of charge, without right  
+to sublicense.. The *Software* may be used "non-commercially", i.e., for research  
+and/or evaluation purposes only.  
+
+Subject to the terms and conditions of this License, you are granted a  
+non-exclusive, royalty-free, license to reproduce, prepare derivative works of,  
+publicly display, publicly perform and distribute its *Work* and any resulting  
+derivative works in any form.  
+
+## 4.  Limitations  
+
+**4.1 Redistribution.** You may reproduce or distribute the *Work* only if (a) you do  
+so under this License, (b) you include a complete copy of this License with  
+your distribution, and (c) you retain without modification any copyright,  
+patent, trademark, or attribution notices that are present in the *Work*.  
+
+**4.2 Derivative Works.** You may specify that additional or different terms apply  
+to the use, reproduction, and distribution of your derivative works of the *Work*  
+("Your Terms") only if (a) Your Terms provide that the use limitation in  
+Section 2 applies to your derivative works, and (b) you identify the specific  
+derivative works that are subject to Your Terms. Notwithstanding Your Terms,  
+this License (including the redistribution requirements in Section 3.1) will  
+continue to apply to the *Work* itself.  
+
+**4.3** Any other use without of prior consent of Licensors is prohibited. Research  
+users explicitly acknowledge having received from Licensors all information  
+allowing to appreciate the adequacy between of the *Software* and their needs and  
+to undertake all necessary precautions for its execution and use.  
+
+**4.4** The *Software* is provided both as a compiled library file and as source  
+code. In case of using the *Software* for a publication or other results obtained  
+through the use of the *Software*, users are strongly encouraged to cite the  
+corresponding publications as explained in the documentation of the *Software*.  
+
+## 5.  Disclaimer  
+
+THE USER CANNOT USE, EXPLOIT OR DISTRIBUTE THE *SOFTWARE* FOR COMMERCIAL PURPOSES  
+WITHOUT PRIOR AND EXPLICIT CONSENT OF LICENSORS. YOU MUST CONTACT INRIA FOR ANY  
+UNAUTHORIZED USE: stip-sophia.transfert@inria.fr . ANY SUCH ACTION WILL  
+CONSTITUTE A FORGERY. THIS *SOFTWARE* IS PROVIDED "AS IS" WITHOUT ANY WARRANTIES  
+OF ANY NATURE AND ANY EXPRESS OR IMPLIED WARRANTIES, WITH REGARDS TO COMMERCIAL  
+USE, PROFESSIONNAL USE, LEGAL OR NOT, OR OTHER, OR COMMERCIALISATION OR  
+ADAPTATION. UNLESS EXPLICITLY PROVIDED BY LAW, IN NO EVENT, SHALL INRIA OR THE  
+AUTHOR BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR  
+CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE  
+GOODS OR SERVICES, LOSS OF USE, DATA, OR PROFITS OR BUSINESS INTERRUPTION)  
+HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT  
+LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING FROM, OUT OF OR  
+IN CONNECTION WITH THE *SOFTWARE* OR THE USE OR OTHER DEALINGS IN THE *SOFTWARE*.  
\ No newline at end of file
diff --git a/src/third-party/diff-gaussian-rasterization-w-depth/README.md b/src/third-party/diff-gaussian-rasterization-w-depth/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..0f8764d98871b7b03edab8a47af48bf22034a14a
--- /dev/null
+++ b/src/third-party/diff-gaussian-rasterization-w-depth/README.md
@@ -0,0 +1,47 @@
+
+This is a clone of https://github.com/graphdeco-inria/diff-gaussian-rasterization/tree/59f5f77e3ddbac3ed9db93ec2cfe99ed6c5d121d
+
+However, it has been edited by Jonathon Luiten to also render 'depth' as well as colour.
+
+This is needed for Jonathon's Dynamic 3D Gaussians work which can be found here: http://dynamic3dgaussians.github.io
+
+By default, the depth is calculated as 'median depth', where the depth is the depth of the Gaussian center which causes the accumulated rays transmittance to drop below 0.5.
+If a ray doesn't reach this threshold it is given a default depth of 15. This median depth avoids the depth floaters around depth boundaries that 'mean depth' would give.
+If 'mean depth' is preffered, there is commented out code which also calculates 'mean depth'.
+See lines 307-308 and 363-372 of cuda_rasterizer/forward.cu.
+
+Note that the backward pass for the depth has not been implemented, so it won't work for training with depth ground-truth.
+
+Note that the code in this repo follows the (non commercial) license of Inria as laid out in LICENSE.md
+
+If you're using this as part of the Dynamic 3D Gaussians code, just follow the installation instruction for that codebase.
+
+To install this stand-alone I have been doing the following (although I don't think this is necessarily the best way):
+```
+git clone git@github.com:git@github.com:JonathonLuiten/diff-gaussian-rasterization-w-depth.git
+cd diff-gaussian-rasterization-w-depth
+python setup.py install
+pip install .
+```
+
+Original readme below:
+
+# Differential Gaussian Rasterization
+
+Used as the rasterization engine for the paper "3D Gaussian Splatting for Real-Time Rendering of Radiance Fields". If you can make use of it in your own research, please be so kind to cite us.
+
+<section class="section" id="BibTeX">
+  <div class="container is-max-desktop content">
+    <h2 class="title">BibTeX</h2>
+    <pre><code>@Article{kerbl3Dgaussians,
+      author       = {Kerbl, Bernhard and Kopanas, Georgios and Leimk{\"u}hler, Thomas and Drettakis, George},
+      title        = {3D Gaussian Splatting for Real-Time Radiance Field Rendering},
+      journal      = {ACM Transactions on Graphics},
+      number       = {4},
+      volume       = {42},
+      month        = {July},
+      year         = {2023},
+      url          = {https://repo-sam.inria.fr/fungraph/3d-gaussian-splatting/}
+}</code></pre>
+  </div>
+</section>
\ No newline at end of file
diff --git a/src/third-party/diff-gaussian-rasterization-w-depth/cuda_rasterizer/auxiliary.h b/src/third-party/diff-gaussian-rasterization-w-depth/cuda_rasterizer/auxiliary.h
new file mode 100644
index 0000000000000000000000000000000000000000..f5682ff993bbb7837896a94b32c23a51c6ccdc27
--- /dev/null
+++ b/src/third-party/diff-gaussian-rasterization-w-depth/cuda_rasterizer/auxiliary.h
@@ -0,0 +1,167 @@
+/*
+ * Copyright (C) 2023, Inria
+ * GRAPHDECO research group, https://team.inria.fr/graphdeco
+ * All rights reserved.
+ *
+ * This software is free for non-commercial, research and evaluation use 
+ * under the terms of the LICENSE.md file.
+ *
+ * For inquiries contact  george.drettakis@inria.fr
+ */
+
+#ifndef CUDA_RASTERIZER_AUXILIARY_H_INCLUDED
+#define CUDA_RASTERIZER_AUXILIARY_H_INCLUDED
+
+#include "config.h"
+#include "stdio.h"
+
+#define BLOCK_SIZE (BLOCK_X * BLOCK_Y)
+#define NUM_WARPS (BLOCK_SIZE/32)
+
+// Spherical harmonics coefficients
+__device__ const float SH_C0 = 0.28209479177387814f;
+__device__ const float SH_C1 = 0.4886025119029199f;
+__device__ const float SH_C2[] = {
+	1.0925484305920792f,
+	-1.0925484305920792f,
+	0.31539156525252005f,
+	-1.0925484305920792f,
+	0.5462742152960396f
+};
+__device__ const float SH_C3[] = {
+	-0.5900435899266435f,
+	2.890611442640554f,
+	-0.4570457994644658f,
+	0.3731763325901154f,
+	-0.4570457994644658f,
+	1.445305721320277f,
+	-0.5900435899266435f
+};
+
+__forceinline__ __device__ float ndc2Pix(float v, int S)
+{
+	return ((v + 1.0) * S - 1.0) * 0.5;
+}
+
+__forceinline__ __device__ void getRect(const float2 p, int max_radius, uint2& rect_min, uint2& rect_max, dim3 grid)
+{
+	rect_min = {
+		min(grid.x, max((int)0, (int)((p.x - max_radius) / BLOCK_X))),
+		min(grid.y, max((int)0, (int)((p.y - max_radius) / BLOCK_Y)))
+	};
+	rect_max = {
+		min(grid.x, max((int)0, (int)((p.x + max_radius + BLOCK_X - 1) / BLOCK_X))),
+		min(grid.y, max((int)0, (int)((p.y + max_radius + BLOCK_Y - 1) / BLOCK_Y)))
+	};
+}
+
+__forceinline__ __device__ float3 transformPoint4x3(const float3& p, const float* matrix)
+{
+	float3 transformed = {
+		matrix[0] * p.x + matrix[4] * p.y + matrix[8] * p.z + matrix[12],
+		matrix[1] * p.x + matrix[5] * p.y + matrix[9] * p.z + matrix[13],
+		matrix[2] * p.x + matrix[6] * p.y + matrix[10] * p.z + matrix[14],
+	};
+	return transformed;
+}
+
+__forceinline__ __device__ float4 transformPoint4x4(const float3& p, const float* matrix)
+{
+	float4 transformed = {
+		matrix[0] * p.x + matrix[4] * p.y + matrix[8] * p.z + matrix[12],
+		matrix[1] * p.x + matrix[5] * p.y + matrix[9] * p.z + matrix[13],
+		matrix[2] * p.x + matrix[6] * p.y + matrix[10] * p.z + matrix[14],
+		matrix[3] * p.x + matrix[7] * p.y + matrix[11] * p.z + matrix[15]
+	};
+	return transformed;
+}
+
+__forceinline__ __device__ float3 transformVec4x3(const float3& p, const float* matrix)
+{
+	float3 transformed = {
+		matrix[0] * p.x + matrix[4] * p.y + matrix[8] * p.z,
+		matrix[1] * p.x + matrix[5] * p.y + matrix[9] * p.z,
+		matrix[2] * p.x + matrix[6] * p.y + matrix[10] * p.z,
+	};
+	return transformed;
+}
+
+__forceinline__ __device__ float3 transformVec4x3Transpose(const float3& p, const float* matrix)
+{
+	float3 transformed = {
+		matrix[0] * p.x + matrix[1] * p.y + matrix[2] * p.z,
+		matrix[4] * p.x + matrix[5] * p.y + matrix[6] * p.z,
+		matrix[8] * p.x + matrix[9] * p.y + matrix[10] * p.z,
+	};
+	return transformed;
+}
+
+__forceinline__ __device__ float dnormvdz(float3 v, float3 dv)
+{
+	float sum2 = v.x * v.x + v.y * v.y + v.z * v.z;
+	float invsum32 = 1.0f / sqrt(sum2 * sum2 * sum2);
+	float dnormvdz = (-v.x * v.z * dv.x - v.y * v.z * dv.y + (sum2 - v.z * v.z) * dv.z) * invsum32;
+	return dnormvdz;
+}
+
+__forceinline__ __device__ float3 dnormvdv(float3 v, float3 dv)
+{
+	float sum2 = v.x * v.x + v.y * v.y + v.z * v.z;
+	float invsum32 = 1.0f / sqrt(sum2 * sum2 * sum2);
+
+	float3 dnormvdv;
+	dnormvdv.x = ((+sum2 - v.x * v.x) * dv.x - v.y * v.x * dv.y - v.z * v.x * dv.z) * invsum32;
+	dnormvdv.y = (-v.x * v.y * dv.x + (sum2 - v.y * v.y) * dv.y - v.z * v.y * dv.z) * invsum32;
+	dnormvdv.z = (-v.x * v.z * dv.x - v.y * v.z * dv.y + (sum2 - v.z * v.z) * dv.z) * invsum32;
+	return dnormvdv;
+}
+
+__forceinline__ __device__ float4 dnormvdv(float4 v, float4 dv)
+{
+	float sum2 = v.x * v.x + v.y * v.y + v.z * v.z + v.w * v.w;
+	float invsum32 = 1.0f / sqrt(sum2 * sum2 * sum2);
+
+	float4 vdv = { v.x * dv.x, v.y * dv.y, v.z * dv.z, v.w * dv.w };
+	float vdv_sum = vdv.x + vdv.y + vdv.z + vdv.w;
+	float4 dnormvdv;
+	dnormvdv.x = ((sum2 - v.x * v.x) * dv.x - v.x * (vdv_sum - vdv.x)) * invsum32;
+	dnormvdv.y = ((sum2 - v.y * v.y) * dv.y - v.y * (vdv_sum - vdv.y)) * invsum32;
+	dnormvdv.z = ((sum2 - v.z * v.z) * dv.z - v.z * (vdv_sum - vdv.z)) * invsum32;
+	dnormvdv.w = ((sum2 - v.w * v.w) * dv.w - v.w * (vdv_sum - vdv.w)) * invsum32;
+	return dnormvdv;
+}
+
+__forceinline__ __device__ float sigmoid(float x)
+{
+	return 1.0f / (1.0f + expf(-x));
+}
+
+__forceinline__ __device__ bool in_frustum(int idx,
+	const float* orig_points,
+	const float* viewmatrix,
+	const float* projmatrix,
+	const float z_threshold,
+	bool prefiltered,
+	float3& p_view)
+{
+	float3 p_orig = { orig_points[3 * idx], orig_points[3 * idx + 1], orig_points[3 * idx + 2] };
+
+	// Bring points to screen space
+	float4 p_hom = transformPoint4x4(p_orig, projmatrix);
+	float p_w = 1.0f / (p_hom.w + 0.0000001f);
+	float3 p_proj = { p_hom.x * p_w, p_hom.y * p_w, p_hom.z * p_w };
+	p_view = transformPoint4x3(p_orig, viewmatrix);
+
+	if (p_view.z <= z_threshold) // || ((p_proj.x < -1.3 || p_proj.x > 1.3 || p_proj.y < -1.3 || p_proj.y > 1.3)))
+	{
+		if (prefiltered)
+		{
+			printf("Point is filtered although prefiltered is set. This shouldn't happen!");
+			__trap();
+		}
+		return false;
+	}
+	return true;
+}
+
+#endif
\ No newline at end of file
diff --git a/src/third-party/diff-gaussian-rasterization-w-depth/cuda_rasterizer/backward.cu b/src/third-party/diff-gaussian-rasterization-w-depth/cuda_rasterizer/backward.cu
new file mode 100644
index 0000000000000000000000000000000000000000..4aa41e1cb856e429dc47ff4e4380ee76f96aaebe
--- /dev/null
+++ b/src/third-party/diff-gaussian-rasterization-w-depth/cuda_rasterizer/backward.cu
@@ -0,0 +1,657 @@
+/*
+ * Copyright (C) 2023, Inria
+ * GRAPHDECO research group, https://team.inria.fr/graphdeco
+ * All rights reserved.
+ *
+ * This software is free for non-commercial, research and evaluation use 
+ * under the terms of the LICENSE.md file.
+ *
+ * For inquiries contact  george.drettakis@inria.fr
+ */
+
+#include "backward.h"
+#include "auxiliary.h"
+#include <cooperative_groups.h>
+#include <cooperative_groups/reduce.h>
+namespace cg = cooperative_groups;
+
+// Backward pass for conversion of spherical harmonics to RGB for
+// each Gaussian.
+__device__ void computeColorFromSH(int idx, int deg, int max_coeffs, const glm::vec3* means, glm::vec3 campos, const float* shs, const bool* clamped, const glm::vec3* dL_dcolor, glm::vec3* dL_dmeans, glm::vec3* dL_dshs)
+{
+	// Compute intermediate values, as it is done during forward
+	glm::vec3 pos = means[idx];
+	glm::vec3 dir_orig = pos - campos;
+	glm::vec3 dir = dir_orig / glm::length(dir_orig);
+
+	glm::vec3* sh = ((glm::vec3*)shs) + idx * max_coeffs;
+
+	// Use PyTorch rule for clamping: if clamping was applied,
+	// gradient becomes 0.
+	glm::vec3 dL_dRGB = dL_dcolor[idx];
+	dL_dRGB.x *= clamped[3 * idx + 0] ? 0 : 1;
+	dL_dRGB.y *= clamped[3 * idx + 1] ? 0 : 1;
+	dL_dRGB.z *= clamped[3 * idx + 2] ? 0 : 1;
+
+	glm::vec3 dRGBdx(0, 0, 0);
+	glm::vec3 dRGBdy(0, 0, 0);
+	glm::vec3 dRGBdz(0, 0, 0);
+	float x = dir.x;
+	float y = dir.y;
+	float z = dir.z;
+
+	// Target location for this Gaussian to write SH gradients to
+	glm::vec3* dL_dsh = dL_dshs + idx * max_coeffs;
+
+	// No tricks here, just high school-level calculus.
+	float dRGBdsh0 = SH_C0;
+	dL_dsh[0] = dRGBdsh0 * dL_dRGB;
+	if (deg > 0)
+	{
+		float dRGBdsh1 = -SH_C1 * y;
+		float dRGBdsh2 = SH_C1 * z;
+		float dRGBdsh3 = -SH_C1 * x;
+		dL_dsh[1] = dRGBdsh1 * dL_dRGB;
+		dL_dsh[2] = dRGBdsh2 * dL_dRGB;
+		dL_dsh[3] = dRGBdsh3 * dL_dRGB;
+
+		dRGBdx = -SH_C1 * sh[3];
+		dRGBdy = -SH_C1 * sh[1];
+		dRGBdz = SH_C1 * sh[2];
+
+		if (deg > 1)
+		{
+			float xx = x * x, yy = y * y, zz = z * z;
+			float xy = x * y, yz = y * z, xz = x * z;
+
+			float dRGBdsh4 = SH_C2[0] * xy;
+			float dRGBdsh5 = SH_C2[1] * yz;
+			float dRGBdsh6 = SH_C2[2] * (2.f * zz - xx - yy);
+			float dRGBdsh7 = SH_C2[3] * xz;
+			float dRGBdsh8 = SH_C2[4] * (xx - yy);
+			dL_dsh[4] = dRGBdsh4 * dL_dRGB;
+			dL_dsh[5] = dRGBdsh5 * dL_dRGB;
+			dL_dsh[6] = dRGBdsh6 * dL_dRGB;
+			dL_dsh[7] = dRGBdsh7 * dL_dRGB;
+			dL_dsh[8] = dRGBdsh8 * dL_dRGB;
+
+			dRGBdx += SH_C2[0] * y * sh[4] + SH_C2[2] * 2.f * -x * sh[6] + SH_C2[3] * z * sh[7] + SH_C2[4] * 2.f * x * sh[8];
+			dRGBdy += SH_C2[0] * x * sh[4] + SH_C2[1] * z * sh[5] + SH_C2[2] * 2.f * -y * sh[6] + SH_C2[4] * 2.f * -y * sh[8];
+			dRGBdz += SH_C2[1] * y * sh[5] + SH_C2[2] * 2.f * 2.f * z * sh[6] + SH_C2[3] * x * sh[7];
+
+			if (deg > 2)
+			{
+				float dRGBdsh9 = SH_C3[0] * y * (3.f * xx - yy);
+				float dRGBdsh10 = SH_C3[1] * xy * z;
+				float dRGBdsh11 = SH_C3[2] * y * (4.f * zz - xx - yy);
+				float dRGBdsh12 = SH_C3[3] * z * (2.f * zz - 3.f * xx - 3.f * yy);
+				float dRGBdsh13 = SH_C3[4] * x * (4.f * zz - xx - yy);
+				float dRGBdsh14 = SH_C3[5] * z * (xx - yy);
+				float dRGBdsh15 = SH_C3[6] * x * (xx - 3.f * yy);
+				dL_dsh[9] = dRGBdsh9 * dL_dRGB;
+				dL_dsh[10] = dRGBdsh10 * dL_dRGB;
+				dL_dsh[11] = dRGBdsh11 * dL_dRGB;
+				dL_dsh[12] = dRGBdsh12 * dL_dRGB;
+				dL_dsh[13] = dRGBdsh13 * dL_dRGB;
+				dL_dsh[14] = dRGBdsh14 * dL_dRGB;
+				dL_dsh[15] = dRGBdsh15 * dL_dRGB;
+
+				dRGBdx += (
+					SH_C3[0] * sh[9] * 3.f * 2.f * xy +
+					SH_C3[1] * sh[10] * yz +
+					SH_C3[2] * sh[11] * -2.f * xy +
+					SH_C3[3] * sh[12] * -3.f * 2.f * xz +
+					SH_C3[4] * sh[13] * (-3.f * xx + 4.f * zz - yy) +
+					SH_C3[5] * sh[14] * 2.f * xz +
+					SH_C3[6] * sh[15] * 3.f * (xx - yy));
+
+				dRGBdy += (
+					SH_C3[0] * sh[9] * 3.f * (xx - yy) +
+					SH_C3[1] * sh[10] * xz +
+					SH_C3[2] * sh[11] * (-3.f * yy + 4.f * zz - xx) +
+					SH_C3[3] * sh[12] * -3.f * 2.f * yz +
+					SH_C3[4] * sh[13] * -2.f * xy +
+					SH_C3[5] * sh[14] * -2.f * yz +
+					SH_C3[6] * sh[15] * -3.f * 2.f * xy);
+
+				dRGBdz += (
+					SH_C3[1] * sh[10] * xy +
+					SH_C3[2] * sh[11] * 4.f * 2.f * yz +
+					SH_C3[3] * sh[12] * 3.f * (2.f * zz - xx - yy) +
+					SH_C3[4] * sh[13] * 4.f * 2.f * xz +
+					SH_C3[5] * sh[14] * (xx - yy));
+			}
+		}
+	}
+
+	// The view direction is an input to the computation. View direction
+	// is influenced by the Gaussian's mean, so SHs gradients
+	// must propagate back into 3D position.
+	glm::vec3 dL_ddir(glm::dot(dRGBdx, dL_dRGB), glm::dot(dRGBdy, dL_dRGB), glm::dot(dRGBdz, dL_dRGB));
+
+	// Account for normalization of direction
+	float3 dL_dmean = dnormvdv(float3{ dir_orig.x, dir_orig.y, dir_orig.z }, float3{ dL_ddir.x, dL_ddir.y, dL_ddir.z });
+
+	// Gradients of loss w.r.t. Gaussian means, but only the portion 
+	// that is caused because the mean affects the view-dependent color.
+	// Additional mean gradient is accumulated in below methods.
+	dL_dmeans[idx] += glm::vec3(dL_dmean.x, dL_dmean.y, dL_dmean.z);
+}
+
+// Backward version of INVERSE 2D covariance matrix computation
+// (due to length launched as separate kernel before other 
+// backward steps contained in preprocess)
+__global__ void computeCov2DCUDA(int P,
+	const float3* means,
+	const int* radii,
+	const float* cov3Ds,
+	const float h_x, float h_y,
+	const float tan_fovx, float tan_fovy,
+	const float* view_matrix,
+	const float* dL_dconics,
+	float3* dL_dmeans,
+	float* dL_dcov)
+{
+	auto idx = cg::this_grid().thread_rank();
+	if (idx >= P || !(radii[idx] > 0))
+		return;
+
+	// Reading location of 3D covariance for this Gaussian
+	const float* cov3D = cov3Ds + 6 * idx;
+
+	// Fetch gradients, recompute 2D covariance and relevant 
+	// intermediate forward results needed in the backward.
+	float3 mean = means[idx];
+	float3 dL_dconic = { dL_dconics[4 * idx], dL_dconics[4 * idx + 1], dL_dconics[4 * idx + 3] };
+	float3 t = transformPoint4x3(mean, view_matrix);
+	
+	const float limx = 1.3f * tan_fovx;
+	const float limy = 1.3f * tan_fovy;
+	const float txtz = t.x / t.z;
+	const float tytz = t.y / t.z;
+	t.x = min(limx, max(-limx, txtz)) * t.z;
+	t.y = min(limy, max(-limy, tytz)) * t.z;
+	
+	const float x_grad_mul = txtz < -limx || txtz > limx ? 0 : 1;
+	const float y_grad_mul = tytz < -limy || tytz > limy ? 0 : 1;
+
+	glm::mat3 J = glm::mat3(h_x / t.z, 0.0f, -(h_x * t.x) / (t.z * t.z),
+		0.0f, h_y / t.z, -(h_y * t.y) / (t.z * t.z),
+		0, 0, 0);
+
+	glm::mat3 W = glm::mat3(
+		view_matrix[0], view_matrix[4], view_matrix[8],
+		view_matrix[1], view_matrix[5], view_matrix[9],
+		view_matrix[2], view_matrix[6], view_matrix[10]);
+
+	glm::mat3 Vrk = glm::mat3(
+		cov3D[0], cov3D[1], cov3D[2],
+		cov3D[1], cov3D[3], cov3D[4],
+		cov3D[2], cov3D[4], cov3D[5]);
+
+	glm::mat3 T = W * J;
+
+	glm::mat3 cov2D = glm::transpose(T) * glm::transpose(Vrk) * T;
+
+	// Use helper variables for 2D covariance entries. More compact.
+	float a = cov2D[0][0] += 0.3f;
+	float b = cov2D[0][1];
+	float c = cov2D[1][1] += 0.3f;
+
+	float denom = a * c - b * b;
+	float dL_da = 0, dL_db = 0, dL_dc = 0;
+	float denom2inv = 1.0f / ((denom * denom) + 0.0000001f);
+
+	if (denom2inv != 0)
+	{
+		// Gradients of loss w.r.t. entries of 2D covariance matrix,
+		// given gradients of loss w.r.t. conic matrix (inverse covariance matrix).
+		// e.g., dL / da = dL / d_conic_a * d_conic_a / d_a
+		dL_da = denom2inv * (-c * c * dL_dconic.x + 2 * b * c * dL_dconic.y + (denom - a * c) * dL_dconic.z);
+		dL_dc = denom2inv * (-a * a * dL_dconic.z + 2 * a * b * dL_dconic.y + (denom - a * c) * dL_dconic.x);
+		dL_db = denom2inv * 2 * (b * c * dL_dconic.x - (denom + 2 * b * b) * dL_dconic.y + a * b * dL_dconic.z);
+
+		// Gradients of loss L w.r.t. each 3D covariance matrix (Vrk) entry, 
+		// given gradients w.r.t. 2D covariance matrix (diagonal).
+		// cov2D = transpose(T) * transpose(Vrk) * T;
+		dL_dcov[6 * idx + 0] = (T[0][0] * T[0][0] * dL_da + T[0][0] * T[1][0] * dL_db + T[1][0] * T[1][0] * dL_dc);
+		dL_dcov[6 * idx + 3] = (T[0][1] * T[0][1] * dL_da + T[0][1] * T[1][1] * dL_db + T[1][1] * T[1][1] * dL_dc);
+		dL_dcov[6 * idx + 5] = (T[0][2] * T[0][2] * dL_da + T[0][2] * T[1][2] * dL_db + T[1][2] * T[1][2] * dL_dc);
+
+		// Gradients of loss L w.r.t. each 3D covariance matrix (Vrk) entry, 
+		// given gradients w.r.t. 2D covariance matrix (off-diagonal).
+		// Off-diagonal elements appear twice --> double the gradient.
+		// cov2D = transpose(T) * transpose(Vrk) * T;
+		dL_dcov[6 * idx + 1] = 2 * T[0][0] * T[0][1] * dL_da + (T[0][0] * T[1][1] + T[0][1] * T[1][0]) * dL_db + 2 * T[1][0] * T[1][1] * dL_dc;
+		dL_dcov[6 * idx + 2] = 2 * T[0][0] * T[0][2] * dL_da + (T[0][0] * T[1][2] + T[0][2] * T[1][0]) * dL_db + 2 * T[1][0] * T[1][2] * dL_dc;
+		dL_dcov[6 * idx + 4] = 2 * T[0][2] * T[0][1] * dL_da + (T[0][1] * T[1][2] + T[0][2] * T[1][1]) * dL_db + 2 * T[1][1] * T[1][2] * dL_dc;
+	}
+	else
+	{
+		for (int i = 0; i < 6; i++)
+			dL_dcov[6 * idx + i] = 0;
+	}
+
+	// Gradients of loss w.r.t. upper 2x3 portion of intermediate matrix T
+	// cov2D = transpose(T) * transpose(Vrk) * T;
+	float dL_dT00 = 2 * (T[0][0] * Vrk[0][0] + T[0][1] * Vrk[0][1] + T[0][2] * Vrk[0][2]) * dL_da +
+		(T[1][0] * Vrk[0][0] + T[1][1] * Vrk[0][1] + T[1][2] * Vrk[0][2]) * dL_db;
+	float dL_dT01 = 2 * (T[0][0] * Vrk[1][0] + T[0][1] * Vrk[1][1] + T[0][2] * Vrk[1][2]) * dL_da +
+		(T[1][0] * Vrk[1][0] + T[1][1] * Vrk[1][1] + T[1][2] * Vrk[1][2]) * dL_db;
+	float dL_dT02 = 2 * (T[0][0] * Vrk[2][0] + T[0][1] * Vrk[2][1] + T[0][2] * Vrk[2][2]) * dL_da +
+		(T[1][0] * Vrk[2][0] + T[1][1] * Vrk[2][1] + T[1][2] * Vrk[2][2]) * dL_db;
+	float dL_dT10 = 2 * (T[1][0] * Vrk[0][0] + T[1][1] * Vrk[0][1] + T[1][2] * Vrk[0][2]) * dL_dc +
+		(T[0][0] * Vrk[0][0] + T[0][1] * Vrk[0][1] + T[0][2] * Vrk[0][2]) * dL_db;
+	float dL_dT11 = 2 * (T[1][0] * Vrk[1][0] + T[1][1] * Vrk[1][1] + T[1][2] * Vrk[1][2]) * dL_dc +
+		(T[0][0] * Vrk[1][0] + T[0][1] * Vrk[1][1] + T[0][2] * Vrk[1][2]) * dL_db;
+	float dL_dT12 = 2 * (T[1][0] * Vrk[2][0] + T[1][1] * Vrk[2][1] + T[1][2] * Vrk[2][2]) * dL_dc +
+		(T[0][0] * Vrk[2][0] + T[0][1] * Vrk[2][1] + T[0][2] * Vrk[2][2]) * dL_db;
+
+	// Gradients of loss w.r.t. upper 3x2 non-zero entries of Jacobian matrix
+	// T = W * J
+	float dL_dJ00 = W[0][0] * dL_dT00 + W[0][1] * dL_dT01 + W[0][2] * dL_dT02;
+	float dL_dJ02 = W[2][0] * dL_dT00 + W[2][1] * dL_dT01 + W[2][2] * dL_dT02;
+	float dL_dJ11 = W[1][0] * dL_dT10 + W[1][1] * dL_dT11 + W[1][2] * dL_dT12;
+	float dL_dJ12 = W[2][0] * dL_dT10 + W[2][1] * dL_dT11 + W[2][2] * dL_dT12;
+
+	float tz = 1.f / t.z;
+	float tz2 = tz * tz;
+	float tz3 = tz2 * tz;
+
+	// Gradients of loss w.r.t. transformed Gaussian mean t
+	float dL_dtx = x_grad_mul * -h_x * tz2 * dL_dJ02;
+	float dL_dty = y_grad_mul * -h_y * tz2 * dL_dJ12;
+	float dL_dtz = -h_x * tz2 * dL_dJ00 - h_y * tz2 * dL_dJ11 + (2 * h_x * t.x) * tz3 * dL_dJ02 + (2 * h_y * t.y) * tz3 * dL_dJ12;
+
+	// Account for transformation of mean to t
+	// t = transformPoint4x3(mean, view_matrix);
+	float3 dL_dmean = transformVec4x3Transpose({ dL_dtx, dL_dty, dL_dtz }, view_matrix);
+
+	// Gradients of loss w.r.t. Gaussian means, but only the portion 
+	// that is caused because the mean affects the covariance matrix.
+	// Additional mean gradient is accumulated in BACKWARD::preprocess.
+	dL_dmeans[idx] = dL_dmean;
+}
+
+// Backward pass for the conversion of scale and rotation to a 
+// 3D covariance matrix for each Gaussian. 
+__device__ void computeCov3D(int idx, const glm::vec3 scale, float mod, const glm::vec4 rot, const float* dL_dcov3Ds, glm::vec3* dL_dscales, glm::vec4* dL_drots)
+{
+	// Recompute (intermediate) results for the 3D covariance computation.
+	glm::vec4 q = rot;// / glm::length(rot);
+	float r = q.x;
+	float x = q.y;
+	float y = q.z;
+	float z = q.w;
+
+	glm::mat3 R = glm::mat3(
+		1.f - 2.f * (y * y + z * z), 2.f * (x * y - r * z), 2.f * (x * z + r * y),
+		2.f * (x * y + r * z), 1.f - 2.f * (x * x + z * z), 2.f * (y * z - r * x),
+		2.f * (x * z - r * y), 2.f * (y * z + r * x), 1.f - 2.f * (x * x + y * y)
+	);
+
+	glm::mat3 S = glm::mat3(1.0f);
+
+	glm::vec3 s = mod * scale;
+	S[0][0] = s.x;
+	S[1][1] = s.y;
+	S[2][2] = s.z;
+
+	glm::mat3 M = S * R;
+
+	const float* dL_dcov3D = dL_dcov3Ds + 6 * idx;
+
+	glm::vec3 dunc(dL_dcov3D[0], dL_dcov3D[3], dL_dcov3D[5]);
+	glm::vec3 ounc = 0.5f * glm::vec3(dL_dcov3D[1], dL_dcov3D[2], dL_dcov3D[4]);
+
+	// Convert per-element covariance loss gradients to matrix form
+	glm::mat3 dL_dSigma = glm::mat3(
+		dL_dcov3D[0], 0.5f * dL_dcov3D[1], 0.5f * dL_dcov3D[2],
+		0.5f * dL_dcov3D[1], dL_dcov3D[3], 0.5f * dL_dcov3D[4],
+		0.5f * dL_dcov3D[2], 0.5f * dL_dcov3D[4], dL_dcov3D[5]
+	);
+
+	// Compute loss gradient w.r.t. matrix M
+	// dSigma_dM = 2 * M
+	glm::mat3 dL_dM = 2.0f * M * dL_dSigma;
+
+	glm::mat3 Rt = glm::transpose(R);
+	glm::mat3 dL_dMt = glm::transpose(dL_dM);
+
+	// Gradients of loss w.r.t. scale
+	glm::vec3* dL_dscale = dL_dscales + idx;
+	dL_dscale->x = glm::dot(Rt[0], dL_dMt[0]);
+	dL_dscale->y = glm::dot(Rt[1], dL_dMt[1]);
+	dL_dscale->z = glm::dot(Rt[2], dL_dMt[2]);
+
+	dL_dMt[0] *= s.x;
+	dL_dMt[1] *= s.y;
+	dL_dMt[2] *= s.z;
+
+	// Gradients of loss w.r.t. normalized quaternion
+	glm::vec4 dL_dq;
+	dL_dq.x = 2 * z * (dL_dMt[0][1] - dL_dMt[1][0]) + 2 * y * (dL_dMt[2][0] - dL_dMt[0][2]) + 2 * x * (dL_dMt[1][2] - dL_dMt[2][1]);
+	dL_dq.y = 2 * y * (dL_dMt[1][0] + dL_dMt[0][1]) + 2 * z * (dL_dMt[2][0] + dL_dMt[0][2]) + 2 * r * (dL_dMt[1][2] - dL_dMt[2][1]) - 4 * x * (dL_dMt[2][2] + dL_dMt[1][1]);
+	dL_dq.z = 2 * x * (dL_dMt[1][0] + dL_dMt[0][1]) + 2 * r * (dL_dMt[2][0] - dL_dMt[0][2]) + 2 * z * (dL_dMt[1][2] + dL_dMt[2][1]) - 4 * y * (dL_dMt[2][2] + dL_dMt[0][0]);
+	dL_dq.w = 2 * r * (dL_dMt[0][1] - dL_dMt[1][0]) + 2 * x * (dL_dMt[2][0] + dL_dMt[0][2]) + 2 * y * (dL_dMt[1][2] + dL_dMt[2][1]) - 4 * z * (dL_dMt[1][1] + dL_dMt[0][0]);
+
+	// Gradients of loss w.r.t. unnormalized quaternion
+	float4* dL_drot = (float4*)(dL_drots + idx);
+	*dL_drot = float4{ dL_dq.x, dL_dq.y, dL_dq.z, dL_dq.w };//dnormvdv(float4{ rot.x, rot.y, rot.z, rot.w }, float4{ dL_dq.x, dL_dq.y, dL_dq.z, dL_dq.w });
+}
+
+// Backward pass of the preprocessing steps, except
+// for the covariance computation and inversion
+// (those are handled by a previous kernel call)
+template<int C>
+__global__ void preprocessCUDA(
+	int P, int D, int M,
+	const float3* means,
+	const int* radii,
+	const float* shs,
+	const bool* clamped,
+	const glm::vec3* scales,
+	const glm::vec4* rotations,
+	const float scale_modifier,
+	const float* proj,
+	const glm::vec3* campos,
+	const float3* dL_dmean2D,
+	glm::vec3* dL_dmeans,
+	float* dL_dcolor,
+	float* dL_dcov3D,
+	float* dL_dsh,
+	glm::vec3* dL_dscale,
+	glm::vec4* dL_drot)
+{
+	auto idx = cg::this_grid().thread_rank();
+	if (idx >= P || !(radii[idx] > 0))
+		return;
+
+	float3 m = means[idx];
+
+	// Taking care of gradients from the screenspace points
+	float4 m_hom = transformPoint4x4(m, proj);
+	float m_w = 1.0f / (m_hom.w + 0.0000001f);
+
+	// Compute loss gradient w.r.t. 3D means due to gradients of 2D means
+	// from rendering procedure
+	glm::vec3 dL_dmean;
+	float mul1 = (proj[0] * m.x + proj[4] * m.y + proj[8] * m.z + proj[12]) * m_w * m_w;
+	float mul2 = (proj[1] * m.x + proj[5] * m.y + proj[9] * m.z + proj[13]) * m_w * m_w;
+	dL_dmean.x = (proj[0] * m_w - proj[3] * mul1) * dL_dmean2D[idx].x + (proj[1] * m_w - proj[3] * mul2) * dL_dmean2D[idx].y;
+	dL_dmean.y = (proj[4] * m_w - proj[7] * mul1) * dL_dmean2D[idx].x + (proj[5] * m_w - proj[7] * mul2) * dL_dmean2D[idx].y;
+	dL_dmean.z = (proj[8] * m_w - proj[11] * mul1) * dL_dmean2D[idx].x + (proj[9] * m_w - proj[11] * mul2) * dL_dmean2D[idx].y;
+
+	// That's the second part of the mean gradient. Previous computation
+	// of cov2D and following SH conversion also affects it.
+	dL_dmeans[idx] += dL_dmean;
+
+	// Compute gradient updates due to computing colors from SHs
+	if (shs)
+		computeColorFromSH(idx, D, M, (glm::vec3*)means, *campos, shs, clamped, (glm::vec3*)dL_dcolor, (glm::vec3*)dL_dmeans, (glm::vec3*)dL_dsh);
+
+	// Compute gradient updates due to computing covariance from scale/rotation
+	if (scales)
+		computeCov3D(idx, scales[idx], scale_modifier, rotations[idx], dL_dcov3D, dL_dscale, dL_drot);
+}
+
+// Backward version of the rendering procedure.
+template <uint32_t C>
+__global__ void __launch_bounds__(BLOCK_X * BLOCK_Y)
+renderCUDA(
+	const uint2* __restrict__ ranges,
+	const uint32_t* __restrict__ point_list,
+	int W, int H,
+	const float* __restrict__ bg_color,
+	const float2* __restrict__ points_xy_image,
+	const float4* __restrict__ conic_opacity,
+	const float* __restrict__ colors,
+	const float* __restrict__ final_Ts,
+	const uint32_t* __restrict__ n_contrib,
+	const float* __restrict__ dL_dpixels,
+	float3* __restrict__ dL_dmean2D,
+	float4* __restrict__ dL_dconic2D,
+	float* __restrict__ dL_dopacity,
+	float* __restrict__ dL_dcolors)
+{
+	// We rasterize again. Compute necessary block info.
+	auto block = cg::this_thread_block();
+	const uint32_t horizontal_blocks = (W + BLOCK_X - 1) / BLOCK_X;
+	const uint2 pix_min = { block.group_index().x * BLOCK_X, block.group_index().y * BLOCK_Y };
+	const uint2 pix_max = { min(pix_min.x + BLOCK_X, W), min(pix_min.y + BLOCK_Y , H) };
+	const uint2 pix = { pix_min.x + block.thread_index().x, pix_min.y + block.thread_index().y };
+	const uint32_t pix_id = W * pix.y + pix.x;
+	const float2 pixf = { (float)pix.x, (float)pix.y };
+
+	const bool inside = pix.x < W&& pix.y < H;
+	const uint2 range = ranges[block.group_index().y * horizontal_blocks + block.group_index().x];
+
+	const int rounds = ((range.y - range.x + BLOCK_SIZE - 1) / BLOCK_SIZE);
+
+	bool done = !inside;
+	int toDo = range.y - range.x;
+
+	__shared__ int collected_id[BLOCK_SIZE];
+	__shared__ float2 collected_xy[BLOCK_SIZE];
+	__shared__ float4 collected_conic_opacity[BLOCK_SIZE];
+	__shared__ float collected_colors[C * BLOCK_SIZE];
+
+	// In the forward, we stored the final value for T, the
+	// product of all (1 - alpha) factors. 
+	const float T_final = inside ? final_Ts[pix_id] : 0;
+	float T = T_final;
+
+	// We start from the back. The ID of the last contributing
+	// Gaussian is known from each pixel from the forward.
+	uint32_t contributor = toDo;
+	const int last_contributor = inside ? n_contrib[pix_id] : 0;
+
+	float accum_rec[C] = { 0 };
+	float dL_dpixel[C];
+	if (inside)
+		for (int i = 0; i < C; i++)
+			dL_dpixel[i] = dL_dpixels[i * H * W + pix_id];
+
+	float last_alpha = 0;
+	float last_color[C] = { 0 };
+
+	// Gradient of pixel coordinate w.r.t. normalized 
+	// screen-space viewport corrdinates (-1 to 1)
+	const float ddelx_dx = 0.5 * W;
+	const float ddely_dy = 0.5 * H;
+
+	// Traverse all Gaussians
+	for (int i = 0; i < rounds; i++, toDo -= BLOCK_SIZE)
+	{
+		// Load auxiliary data into shared memory, start in the BACK
+		// and load them in revers order.
+		block.sync();
+		const int progress = i * BLOCK_SIZE + block.thread_rank();
+		if (range.x + progress < range.y)
+		{
+			const int coll_id = point_list[range.y - progress - 1];
+			collected_id[block.thread_rank()] = coll_id;
+			collected_xy[block.thread_rank()] = points_xy_image[coll_id];
+			collected_conic_opacity[block.thread_rank()] = conic_opacity[coll_id];
+			for (int i = 0; i < C; i++)
+				collected_colors[i * BLOCK_SIZE + block.thread_rank()] = colors[coll_id * C + i];
+		}
+		block.sync();
+
+		// Iterate over Gaussians
+		for (int j = 0; !done && j < min(BLOCK_SIZE, toDo); j++)
+		{
+			// Keep track of current Gaussian ID. Skip, if this one
+			// is behind the last contributor for this pixel.
+			contributor--;
+			if (contributor >= last_contributor)
+				continue;
+
+			// Compute blending values, as before.
+			const float2 xy = collected_xy[j];
+			const float2 d = { xy.x - pixf.x, xy.y - pixf.y };
+			const float4 con_o = collected_conic_opacity[j];
+			const float power = -0.5f * (con_o.x * d.x * d.x + con_o.z * d.y * d.y) - con_o.y * d.x * d.y;
+			if (power > 0.0f)
+				continue;
+
+			const float G = exp(power);
+			const float alpha = min(0.99f, con_o.w * G);
+			if (alpha < 1.0f / 255.0f)
+				continue;
+
+			T = T / (1.f - alpha);
+			const float dchannel_dcolor = alpha * T;
+
+			// Propagate gradients to per-Gaussian colors and keep
+			// gradients w.r.t. alpha (blending factor for a Gaussian/pixel
+			// pair).
+			float dL_dalpha = 0.0f;
+			const int global_id = collected_id[j];
+			for (int ch = 0; ch < C; ch++)
+			{
+				const float c = collected_colors[ch * BLOCK_SIZE + j];
+				// Update last color (to be used in the next iteration)
+				accum_rec[ch] = last_alpha * last_color[ch] + (1.f - last_alpha) * accum_rec[ch];
+				last_color[ch] = c;
+
+				const float dL_dchannel = dL_dpixel[ch];
+				dL_dalpha += (c - accum_rec[ch]) * dL_dchannel;
+				// Update the gradients w.r.t. color of the Gaussian. 
+				// Atomic, since this pixel is just one of potentially
+				// many that were affected by this Gaussian.
+				atomicAdd(&(dL_dcolors[global_id * C + ch]), dchannel_dcolor * dL_dchannel);
+			}
+			dL_dalpha *= T;
+			// Update last alpha (to be used in the next iteration)
+			last_alpha = alpha;
+
+			// Account for fact that alpha also influences how much of
+			// the background color is added if nothing left to blend
+			float bg_dot_dpixel = 0;
+			for (int i = 0; i < C; i++)
+				bg_dot_dpixel += bg_color[i] * dL_dpixel[i];
+			dL_dalpha += (-T_final / (1.f - alpha)) * bg_dot_dpixel;
+
+
+			// Helpful reusable temporary variables
+			const float dL_dG = con_o.w * dL_dalpha;
+			const float gdx = G * d.x;
+			const float gdy = G * d.y;
+			const float dG_ddelx = -gdx * con_o.x - gdy * con_o.y;
+			const float dG_ddely = -gdy * con_o.z - gdx * con_o.y;
+
+			// Update gradients w.r.t. 2D mean position of the Gaussian
+			atomicAdd(&dL_dmean2D[global_id].x, dL_dG * dG_ddelx * ddelx_dx);
+			atomicAdd(&dL_dmean2D[global_id].y, dL_dG * dG_ddely * ddely_dy);
+
+			// Update gradients w.r.t. 2D covariance (2x2 matrix, symmetric)
+			atomicAdd(&dL_dconic2D[global_id].x, -0.5f * gdx * d.x * dL_dG);
+			atomicAdd(&dL_dconic2D[global_id].y, -0.5f * gdx * d.y * dL_dG);
+			atomicAdd(&dL_dconic2D[global_id].w, -0.5f * gdy * d.y * dL_dG);
+
+			// Update gradients w.r.t. opacity of the Gaussian
+			atomicAdd(&(dL_dopacity[global_id]), G * dL_dalpha);
+		}
+	}
+}
+
+void BACKWARD::preprocess(
+	int P, int D, int M,
+	const float3* means3D,
+	const int* radii,
+	const float* shs,
+	const bool* clamped,
+	const glm::vec3* scales,
+	const glm::vec4* rotations,
+	const float scale_modifier,
+	const float* cov3Ds,
+	const float* viewmatrix,
+	const float* projmatrix,
+	const float focal_x, float focal_y,
+	const float tan_fovx, float tan_fovy,
+	const glm::vec3* campos,
+	const float3* dL_dmean2D,
+	const float* dL_dconic,
+	glm::vec3* dL_dmean3D,
+	float* dL_dcolor,
+	float* dL_dcov3D,
+	float* dL_dsh,
+	glm::vec3* dL_dscale,
+	glm::vec4* dL_drot)
+{
+	// Propagate gradients for the path of 2D conic matrix computation. 
+	// Somewhat long, thus it is its own kernel rather than being part of 
+	// "preprocess". When done, loss gradient w.r.t. 3D means has been
+	// modified and gradient w.r.t. 3D covariance matrix has been computed.	
+	computeCov2DCUDA << <(P + 255) / 256, 256 >> > (
+		P,
+		means3D,
+		radii,
+		cov3Ds,
+		focal_x,
+		focal_y,
+		tan_fovx,
+		tan_fovy,
+		viewmatrix,
+		dL_dconic,
+		(float3*)dL_dmean3D,
+		dL_dcov3D);
+
+	// Propagate gradients for remaining steps: finish 3D mean gradients,
+	// propagate color gradients to SH (if desireD), propagate 3D covariance
+	// matrix gradients to scale and rotation.
+	preprocessCUDA<NUM_CHANNELS> << < (P + 255) / 256, 256 >> > (
+		P, D, M,
+		(float3*)means3D,
+		radii,
+		shs,
+		clamped,
+		(glm::vec3*)scales,
+		(glm::vec4*)rotations,
+		scale_modifier,
+		projmatrix,
+		campos,
+		(float3*)dL_dmean2D,
+		(glm::vec3*)dL_dmean3D,
+		dL_dcolor,
+		dL_dcov3D,
+		dL_dsh,
+		dL_dscale,
+		dL_drot);
+}
+
+void BACKWARD::render(
+	const dim3 grid, const dim3 block,
+	const uint2* ranges,
+	const uint32_t* point_list,
+	int W, int H,
+	const float* bg_color,
+	const float2* means2D,
+	const float4* conic_opacity,
+	const float* colors,
+	const float* final_Ts,
+	const uint32_t* n_contrib,
+	const float* dL_dpixels,
+	float3* dL_dmean2D,
+	float4* dL_dconic2D,
+	float* dL_dopacity,
+	float* dL_dcolors)
+{
+	renderCUDA<NUM_CHANNELS> << <grid, block >> >(
+		ranges,
+		point_list,
+		W, H,
+		bg_color,
+		means2D,
+		conic_opacity,
+		colors,
+		final_Ts,
+		n_contrib,
+		dL_dpixels,
+		dL_dmean2D,
+		dL_dconic2D,
+		dL_dopacity,
+		dL_dcolors
+		);
+}
\ No newline at end of file
diff --git a/src/third-party/diff-gaussian-rasterization-w-depth/cuda_rasterizer/backward.h b/src/third-party/diff-gaussian-rasterization-w-depth/cuda_rasterizer/backward.h
new file mode 100644
index 0000000000000000000000000000000000000000..93dd2e4be371d36385b102b13f59f37c1d019223
--- /dev/null
+++ b/src/third-party/diff-gaussian-rasterization-w-depth/cuda_rasterizer/backward.h
@@ -0,0 +1,65 @@
+/*
+ * Copyright (C) 2023, Inria
+ * GRAPHDECO research group, https://team.inria.fr/graphdeco
+ * All rights reserved.
+ *
+ * This software is free for non-commercial, research and evaluation use 
+ * under the terms of the LICENSE.md file.
+ *
+ * For inquiries contact  george.drettakis@inria.fr
+ */
+
+#ifndef CUDA_RASTERIZER_BACKWARD_H_INCLUDED
+#define CUDA_RASTERIZER_BACKWARD_H_INCLUDED
+
+#include <cuda.h>
+#include "cuda_runtime.h"
+#include "device_launch_parameters.h"
+#define GLM_FORCE_CUDA
+#include <glm/glm.hpp>
+
+namespace BACKWARD
+{
+	void render(
+		const dim3 grid, dim3 block,
+		const uint2* ranges,
+		const uint32_t* point_list,
+		int W, int H,
+		const float* bg_color,
+		const float2* means2D,
+		const float4* conic_opacity,
+		const float* colors,
+		const float* final_Ts,
+		const uint32_t* n_contrib,
+		const float* dL_dpixels,
+		float3* dL_dmean2D,
+		float4* dL_dconic2D,
+		float* dL_dopacity,
+		float* dL_dcolors);
+
+	void preprocess(
+		int P, int D, int M,
+		const float3* means,
+		const int* radii,
+		const float* shs,
+		const bool* clamped,
+		const glm::vec3* scales,
+		const glm::vec4* rotations,
+		const float scale_modifier,
+		const float* cov3Ds,
+		const float* view,
+		const float* proj,
+		const float focal_x, float focal_y,
+		const float tan_fovx, float tan_fovy,
+		const glm::vec3* campos,
+		const float3* dL_dmean2D,
+		const float* dL_dconics,
+		glm::vec3* dL_dmeans,
+		float* dL_dcolor,
+		float* dL_dcov3D,
+		float* dL_dsh,
+		glm::vec3* dL_dscale,
+		glm::vec4* dL_drot);
+}
+
+#endif
\ No newline at end of file
diff --git a/src/third-party/diff-gaussian-rasterization-w-depth/cuda_rasterizer/config.h b/src/third-party/diff-gaussian-rasterization-w-depth/cuda_rasterizer/config.h
new file mode 100644
index 0000000000000000000000000000000000000000..2a912fb34824349caadffe435fc1ab4b31e5aa4f
--- /dev/null
+++ b/src/third-party/diff-gaussian-rasterization-w-depth/cuda_rasterizer/config.h
@@ -0,0 +1,19 @@
+/*
+ * Copyright (C) 2023, Inria
+ * GRAPHDECO research group, https://team.inria.fr/graphdeco
+ * All rights reserved.
+ *
+ * This software is free for non-commercial, research and evaluation use 
+ * under the terms of the LICENSE.md file.
+ *
+ * For inquiries contact  george.drettakis@inria.fr
+ */
+
+#ifndef CUDA_RASTERIZER_CONFIG_H_INCLUDED
+#define CUDA_RASTERIZER_CONFIG_H_INCLUDED
+
+#define NUM_CHANNELS 3 // Default 3, RGB
+#define BLOCK_X 16
+#define BLOCK_Y 16
+
+#endif
\ No newline at end of file
diff --git a/src/third-party/diff-gaussian-rasterization-w-depth/cuda_rasterizer/forward.cu b/src/third-party/diff-gaussian-rasterization-w-depth/cuda_rasterizer/forward.cu
new file mode 100644
index 0000000000000000000000000000000000000000..efaf7f0e47373ffefca267ee12078b9956706b21
--- /dev/null
+++ b/src/third-party/diff-gaussian-rasterization-w-depth/cuda_rasterizer/forward.cu
@@ -0,0 +1,481 @@
+/*
+ * Copyright (C) 2023, Inria
+ * GRAPHDECO research group, https://team.inria.fr/graphdeco
+ * All rights reserved.
+ *
+ * This software is free for non-commercial, research and evaluation use 
+ * under the terms of the LICENSE.md file.
+ *
+ * For inquiries contact  george.drettakis@inria.fr
+ */
+
+#include "forward.h"
+#include "auxiliary.h"
+#include <cooperative_groups.h>
+#include <cooperative_groups/reduce.h>
+namespace cg = cooperative_groups;
+
+// Forward method for converting the input spherical harmonics
+// coefficients of each Gaussian to a simple RGB color.
+__device__ glm::vec3 computeColorFromSH(int idx, int deg, int max_coeffs, const glm::vec3* means, glm::vec3 campos, const float* shs, bool* clamped)
+{
+	// The implementation is loosely based on code for 
+	// "Differentiable Point-Based Radiance Fields for 
+	// Efficient View Synthesis" by Zhang et al. (2022)
+	glm::vec3 pos = means[idx];
+	glm::vec3 dir = pos - campos;
+	dir = dir / glm::length(dir);
+
+	glm::vec3* sh = ((glm::vec3*)shs) + idx * max_coeffs;
+	glm::vec3 result = SH_C0 * sh[0];
+
+	if (deg > 0)
+	{
+		float x = dir.x;
+		float y = dir.y;
+		float z = dir.z;
+		result = result - SH_C1 * y * sh[1] + SH_C1 * z * sh[2] - SH_C1 * x * sh[3];
+
+		if (deg > 1)
+		{
+			float xx = x * x, yy = y * y, zz = z * z;
+			float xy = x * y, yz = y * z, xz = x * z;
+			result = result +
+				SH_C2[0] * xy * sh[4] +
+				SH_C2[1] * yz * sh[5] +
+				SH_C2[2] * (2.0f * zz - xx - yy) * sh[6] +
+				SH_C2[3] * xz * sh[7] +
+				SH_C2[4] * (xx - yy) * sh[8];
+
+			if (deg > 2)
+			{
+				result = result +
+					SH_C3[0] * y * (3.0f * xx - yy) * sh[9] +
+					SH_C3[1] * xy * z * sh[10] +
+					SH_C3[2] * y * (4.0f * zz - xx - yy) * sh[11] +
+					SH_C3[3] * z * (2.0f * zz - 3.0f * xx - 3.0f * yy) * sh[12] +
+					SH_C3[4] * x * (4.0f * zz - xx - yy) * sh[13] +
+					SH_C3[5] * z * (xx - yy) * sh[14] +
+					SH_C3[6] * x * (xx - 3.0f * yy) * sh[15];
+			}
+		}
+	}
+	result += 0.5f;
+
+	// RGB colors are clamped to positive values. If values are
+	// clamped, we need to keep track of this for the backward pass.
+	clamped[3 * idx + 0] = (result.x < 0);
+	clamped[3 * idx + 1] = (result.y < 0);
+	clamped[3 * idx + 2] = (result.z < 0);
+	return glm::max(result, 0.0f);
+}
+
+// Forward version of 2D covariance matrix computation
+__device__ float3 computeCov2D(const float3& mean, float focal_x, float focal_y, float tan_fovx, float tan_fovy, const float* cov3D, const float* viewmatrix)
+{
+	// The following models the steps outlined by equations 29
+	// and 31 in "EWA Splatting" (Zwicker et al., 2002). 
+	// Additionally considers aspect / scaling of viewport.
+	// Transposes used to account for row-/column-major conventions.
+	float3 t = transformPoint4x3(mean, viewmatrix);
+
+	const float limx = 1.3f * tan_fovx;
+	const float limy = 1.3f * tan_fovy;
+	const float txtz = t.x / t.z;
+	const float tytz = t.y / t.z;
+	t.x = min(limx, max(-limx, txtz)) * t.z;
+	t.y = min(limy, max(-limy, tytz)) * t.z;
+
+	glm::mat3 J = glm::mat3(
+		focal_x / t.z, 0.0f, -(focal_x * t.x) / (t.z * t.z),
+		0.0f, focal_y / t.z, -(focal_y * t.y) / (t.z * t.z),
+		0, 0, 0);
+
+	glm::mat3 W = glm::mat3(
+		viewmatrix[0], viewmatrix[4], viewmatrix[8],
+		viewmatrix[1], viewmatrix[5], viewmatrix[9],
+		viewmatrix[2], viewmatrix[6], viewmatrix[10]);
+
+	glm::mat3 T = W * J;
+
+	glm::mat3 Vrk = glm::mat3(
+		cov3D[0], cov3D[1], cov3D[2],
+		cov3D[1], cov3D[3], cov3D[4],
+		cov3D[2], cov3D[4], cov3D[5]);
+
+	glm::mat3 cov = glm::transpose(T) * glm::transpose(Vrk) * T;
+
+	// Apply low-pass filter: every Gaussian should be at least
+	// one pixel wide/high. Discard 3rd row and column.
+	cov[0][0] += 0.3f;
+	cov[1][1] += 0.3f;
+	return { float(cov[0][0]), float(cov[0][1]), float(cov[1][1]) };
+}
+
+// Forward method for converting scale and rotation properties of each
+// Gaussian to a 3D covariance matrix in world space. Also takes care
+// of quaternion normalization.
+__device__ void computeCov3D(const glm::vec3 scale, float mod, const glm::vec4 rot, float* cov3D)
+{
+	// Create scaling matrix
+	glm::mat3 S = glm::mat3(1.0f);
+	S[0][0] = mod * scale.x;
+	S[1][1] = mod * scale.y;
+	S[2][2] = mod * scale.z;
+
+	// Normalize quaternion to get valid rotation
+	glm::vec4 q = rot;// / glm::length(rot);
+	float r = q.x;
+	float x = q.y;
+	float y = q.z;
+	float z = q.w;
+
+	// Compute rotation matrix from quaternion
+	glm::mat3 R = glm::mat3(
+		1.f - 2.f * (y * y + z * z), 2.f * (x * y - r * z), 2.f * (x * z + r * y),
+		2.f * (x * y + r * z), 1.f - 2.f * (x * x + z * z), 2.f * (y * z - r * x),
+		2.f * (x * z - r * y), 2.f * (y * z + r * x), 1.f - 2.f * (x * x + y * y)
+	);
+
+	glm::mat3 M = S * R;
+
+	// Compute 3D world covariance matrix Sigma
+	glm::mat3 Sigma = glm::transpose(M) * M;
+
+	// Covariance is symmetric, only store upper right
+	cov3D[0] = Sigma[0][0];
+	cov3D[1] = Sigma[0][1];
+	cov3D[2] = Sigma[0][2];
+	cov3D[3] = Sigma[1][1];
+	cov3D[4] = Sigma[1][2];
+	cov3D[5] = Sigma[2][2];
+}
+
+// Perform initial steps for each Gaussian prior to rasterization.
+template<int C>
+__global__ void preprocessCUDA(int P, int D, int M,
+	const float* orig_points,
+	const glm::vec3* scales,
+	const float scale_modifier,
+	const glm::vec4* rotations,
+	const float* opacities,
+	const float* shs,
+	bool* clamped,
+	const float* cov3D_precomp,
+	const float* colors_precomp,
+	const float* viewmatrix,
+	const float* projmatrix,
+	const glm::vec3* cam_pos,
+	const int W, int H,
+	const float tan_fovx, float tan_fovy,
+	const float focal_x, float focal_y,
+	int* radii,
+	float2* points_xy_image,
+	float* depths,
+	float* cov3Ds,
+	float* rgb,
+	float4* conic_opacity,
+	const dim3 grid,
+	uint32_t* tiles_touched,
+	bool prefiltered,
+	const float z_threshold)
+{
+	auto idx = cg::this_grid().thread_rank();
+	if (idx >= P)
+		return;
+
+	// Initialize radius and touched tiles to 0. If this isn't changed,
+	// this Gaussian will not be processed further.
+	radii[idx] = 0;
+	tiles_touched[idx] = 0;
+
+	// Perform near culling, quit if outside.
+	float3 p_view;
+	if (!in_frustum(idx, orig_points, viewmatrix, projmatrix, z_threshold, prefiltered, p_view))
+		return;
+
+	// Transform point by projecting
+	float3 p_orig = { orig_points[3 * idx], orig_points[3 * idx + 1], orig_points[3 * idx + 2] };
+	float4 p_hom = transformPoint4x4(p_orig, projmatrix);
+	float p_w = 1.0f / (p_hom.w + 0.0000001f);
+	float3 p_proj = { p_hom.x * p_w, p_hom.y * p_w, p_hom.z * p_w };
+
+	// If 3D covariance matrix is precomputed, use it, otherwise compute
+	// from scaling and rotation parameters. 
+	const float* cov3D;
+	if (cov3D_precomp != nullptr)
+	{
+		cov3D = cov3D_precomp + idx * 6;
+	}
+	else
+	{
+		computeCov3D(scales[idx], scale_modifier, rotations[idx], cov3Ds + idx * 6);
+		cov3D = cov3Ds + idx * 6;
+	}
+
+	// Compute 2D screen-space covariance matrix
+	float3 cov = computeCov2D(p_orig, focal_x, focal_y, tan_fovx, tan_fovy, cov3D, viewmatrix);
+
+	// Invert covariance (EWA algorithm)
+	float det = (cov.x * cov.z - cov.y * cov.y);
+	if (det == 0.0f)
+		return;
+	float det_inv = 1.f / det;
+	float3 conic = { cov.z * det_inv, -cov.y * det_inv, cov.x * det_inv };
+
+	// Compute extent in screen space (by finding eigenvalues of
+	// 2D covariance matrix). Use extent to compute a bounding rectangle
+	// of screen-space tiles that this Gaussian overlaps with. Quit if
+	// rectangle covers 0 tiles. 
+	float mid = 0.5f * (cov.x + cov.z);
+	float lambda1 = mid + sqrt(max(0.1f, mid * mid - det));
+	float lambda2 = mid - sqrt(max(0.1f, mid * mid - det));
+	float my_radius = ceil(3.f * sqrt(max(lambda1, lambda2)));
+	float2 point_image = { ndc2Pix(p_proj.x, W), ndc2Pix(p_proj.y, H) };
+	uint2 rect_min, rect_max;
+	getRect(point_image, my_radius, rect_min, rect_max, grid);
+	if ((rect_max.x - rect_min.x) * (rect_max.y - rect_min.y) == 0)
+		return;
+
+	// If colors have been precomputed, use them, otherwise convert
+	// spherical harmonics coefficients to RGB color.
+	if (colors_precomp == nullptr)
+	{
+		glm::vec3 result = computeColorFromSH(idx, D, M, (glm::vec3*)orig_points, *cam_pos, shs, clamped);
+		rgb[idx * C + 0] = result.x;
+		rgb[idx * C + 1] = result.y;
+		rgb[idx * C + 2] = result.z;
+	}
+
+	// Store some useful helper data for the next steps.
+	depths[idx] = p_view.z;
+	radii[idx] = my_radius;
+	points_xy_image[idx] = point_image;
+	// Inverse 2D covariance and opacity neatly pack into one float4
+	conic_opacity[idx] = { conic.x, conic.y, conic.z, opacities[idx] };
+	tiles_touched[idx] = (rect_max.y - rect_min.y) * (rect_max.x - rect_min.x);
+}
+
+// Main rasterization method. Collaboratively works on one tile per
+// block, each thread treats one pixel. Alternates between fetching 
+// and rasterizing data.
+template <uint32_t CHANNELS>
+__global__ void __launch_bounds__(BLOCK_X * BLOCK_Y)
+renderCUDA(
+	const uint2* __restrict__ ranges,
+	const uint32_t* __restrict__ point_list,
+	int W, int H,
+	const float2* __restrict__ points_xy_image,
+	const float* __restrict__ features,
+	const float4* __restrict__ conic_opacity,
+	float* __restrict__ final_T,
+	uint32_t* __restrict__ n_contrib,
+	const float* __restrict__ bg_color,
+	float* __restrict__ out_color,
+	const float* __restrict__ depth,
+	float* __restrict__ out_depth)
+{
+	// Identify current tile and associated min/max pixel range.
+	auto block = cg::this_thread_block();
+	uint32_t horizontal_blocks = (W + BLOCK_X - 1) / BLOCK_X;
+	uint2 pix_min = { block.group_index().x * BLOCK_X, block.group_index().y * BLOCK_Y };
+	uint2 pix_max = { min(pix_min.x + BLOCK_X, W), min(pix_min.y + BLOCK_Y , H) };
+	uint2 pix = { pix_min.x + block.thread_index().x, pix_min.y + block.thread_index().y };
+	uint32_t pix_id = W * pix.y + pix.x;
+	float2 pixf = { (float)pix.x, (float)pix.y };
+
+	// Check if this thread is associated with a valid pixel or outside.
+	bool inside = pix.x < W&& pix.y < H;
+	// Done threads can help with fetching, but don't rasterize
+	bool done = !inside;
+
+	// Load start/end range of IDs to process in bit sorted list.
+	uint2 range = ranges[block.group_index().y * horizontal_blocks + block.group_index().x];
+	const int rounds = ((range.y - range.x + BLOCK_SIZE - 1) / BLOCK_SIZE);
+	int toDo = range.y - range.x;
+
+	// Allocate storage for batches of collectively fetched data.
+	__shared__ int collected_id[BLOCK_SIZE];
+	__shared__ float2 collected_xy[BLOCK_SIZE];
+	__shared__ float4 collected_conic_opacity[BLOCK_SIZE];
+	__shared__ float collected_depth[BLOCK_SIZE];
+
+	// Initialize helper variables
+	float T = 1.0f;
+	uint32_t contributor = 0;
+	uint32_t last_contributor = 0;
+	float C[CHANNELS] = { 0 };
+// 	float D = 0.0f;  // Mean Depth
+    float D = 15.0f;  // Median Depth. TODO: This is a hack setting max_depth to 15
+
+	// Iterate over batches until all done or range is complete
+	for (int i = 0; i < rounds; i++, toDo -= BLOCK_SIZE)
+	{
+		// End if entire block votes that it is done rasterizing
+		int num_done = __syncthreads_count(done);
+		if (num_done == BLOCK_SIZE)
+			break;
+
+		// Collectively fetch per-Gaussian data from global to shared
+		int progress = i * BLOCK_SIZE + block.thread_rank();
+		if (range.x + progress < range.y)
+		{
+			int coll_id = point_list[range.x + progress];
+			collected_id[block.thread_rank()] = coll_id;
+			collected_xy[block.thread_rank()] = points_xy_image[coll_id];
+			collected_conic_opacity[block.thread_rank()] = conic_opacity[coll_id];
+			collected_depth[block.thread_rank()] = depth[coll_id];
+		}
+		block.sync();
+
+		// Iterate over current batch
+		for (int j = 0; !done && j < min(BLOCK_SIZE, toDo); j++)
+		{
+			// Keep track of current position in range
+			contributor++;
+
+			// Resample using conic matrix (cf. "Surface 
+			// Splatting" by Zwicker et al., 2001)
+			float2 xy = collected_xy[j];
+			float2 d = { xy.x - pixf.x, xy.y - pixf.y };
+			float4 con_o = collected_conic_opacity[j];
+			float power = -0.5f * (con_o.x * d.x * d.x + con_o.z * d.y * d.y) - con_o.y * d.x * d.y;
+			if (power > 0.0f)
+				continue;
+
+			// Eq. (2) from 3D Gaussian splatting paper.
+			// Obtain alpha by multiplying with Gaussian opacity
+			// and its exponential falloff from mean.
+			// Avoid numerical instabilities (see paper appendix). 
+			float alpha = min(0.99f, con_o.w * exp(power));
+			if (alpha < 1.0f / 255.0f)
+				continue;
+			float test_T = T * (1 - alpha);
+			if (test_T < 0.0001f)
+			{
+				done = true;
+				continue;
+			}
+
+			// Eq. (3) from 3D Gaussian splatting paper.
+			for (int ch = 0; ch < CHANNELS; ch++)
+				C[ch] += features[collected_id[j] * CHANNELS + ch] * alpha * T;
+
+            // Mean depth:
+//             float dep = collected_depth[j];
+//             D += dep * alpha * T;
+
+            // Median depth:
+            if (T > 0.5f && test_T < 0.5)
+			{
+			    float dep = collected_depth[j];
+				D = dep;
+			}
+
+
+			T = test_T;
+
+			// Keep track of last range entry to update this
+			// pixel.
+			last_contributor = contributor;
+		}
+	}
+
+	// All threads that treat valid pixel write out their final
+	// rendering data to the frame and auxiliary buffers.
+	if (inside)
+	{
+		final_T[pix_id] = T;
+		n_contrib[pix_id] = last_contributor;
+		for (int ch = 0; ch < CHANNELS; ch++)
+			out_color[ch * H * W + pix_id] = C[ch] + T * bg_color[ch];
+		out_depth[pix_id] = D;
+	}
+}
+
+void FORWARD::render(
+	const dim3 grid, dim3 block,
+	const uint2* ranges,
+	const uint32_t* point_list,
+	int W, int H,
+	const float2* means2D,
+	const float* colors,
+	const float4* conic_opacity,
+	float* final_T,
+	uint32_t* n_contrib,
+	const float* bg_color,
+	float* out_color,
+	const float* depth,
+	float* out_depth)
+{
+	renderCUDA<NUM_CHANNELS> << <grid, block >> > (
+		ranges,
+		point_list,
+		W, H,
+		means2D,
+		colors,
+		conic_opacity,
+		final_T,
+		n_contrib,
+		bg_color,
+		out_color,
+		depth,
+		out_depth);
+}
+
+void FORWARD::preprocess(int P, int D, int M,
+	const float* means3D,
+	const glm::vec3* scales,
+	const float scale_modifier,
+	const glm::vec4* rotations,
+	const float* opacities,
+	const float* shs,
+	bool* clamped,
+	const float* cov3D_precomp,
+	const float* colors_precomp,
+	const float* viewmatrix,
+	const float* projmatrix,
+	const glm::vec3* cam_pos,
+	const int W, int H,
+	const float focal_x, float focal_y,
+	const float tan_fovx, float tan_fovy,
+	int* radii,
+	float2* means2D,
+	float* depths,
+	float* cov3Ds,
+	float* rgb,
+	float4* conic_opacity,
+	const dim3 grid,
+	uint32_t* tiles_touched,
+	bool prefiltered,
+	const float z_threshold)
+{
+	preprocessCUDA<NUM_CHANNELS> << <(P + 255) / 256, 256 >> > (
+		P, D, M,
+		means3D,
+		scales,
+		scale_modifier,
+		rotations,
+		opacities,
+		shs,
+		clamped,
+		cov3D_precomp,
+		colors_precomp,
+		viewmatrix, 
+		projmatrix,
+		cam_pos,
+		W, H,
+		tan_fovx, tan_fovy,
+		focal_x, focal_y,
+		radii,
+		means2D,
+		depths,
+		cov3Ds,
+		rgb,
+		conic_opacity,
+		grid,
+		tiles_touched,
+		prefiltered,
+		z_threshold
+		);
+}
\ No newline at end of file
diff --git a/src/third-party/diff-gaussian-rasterization-w-depth/cuda_rasterizer/forward.h b/src/third-party/diff-gaussian-rasterization-w-depth/cuda_rasterizer/forward.h
new file mode 100644
index 0000000000000000000000000000000000000000..7b33fa865ec4c8b28e0923f6f574ecd7ed06a81b
--- /dev/null
+++ b/src/third-party/diff-gaussian-rasterization-w-depth/cuda_rasterizer/forward.h
@@ -0,0 +1,69 @@
+/*
+ * Copyright (C) 2023, Inria
+ * GRAPHDECO research group, https://team.inria.fr/graphdeco
+ * All rights reserved.
+ *
+ * This software is free for non-commercial, research and evaluation use 
+ * under the terms of the LICENSE.md file.
+ *
+ * For inquiries contact  george.drettakis@inria.fr
+ */
+
+#ifndef CUDA_RASTERIZER_FORWARD_H_INCLUDED
+#define CUDA_RASTERIZER_FORWARD_H_INCLUDED
+
+#include <cuda.h>
+#include "cuda_runtime.h"
+#include "device_launch_parameters.h"
+#define GLM_FORCE_CUDA
+#include <glm/glm.hpp>
+
+namespace FORWARD
+{
+	// Perform initial steps for each Gaussian prior to rasterization.
+	void preprocess(int P, int D, int M,
+		const float* orig_points,
+		const glm::vec3* scales,
+		const float scale_modifier,
+		const glm::vec4* rotations,
+		const float* opacities,
+		const float* shs,
+		bool* clamped,
+		const float* cov3D_precomp,
+		const float* colors_precomp,
+		const float* viewmatrix,
+		const float* projmatrix,
+		const glm::vec3* cam_pos,
+		const int W, int H,
+		const float focal_x, float focal_y,
+		const float tan_fovx, float tan_fovy,
+		int* radii,
+		float2* points_xy_image,
+		float* depths,
+		float* cov3Ds,
+		float* colors,
+		float4* conic_opacity,
+		const dim3 grid,
+		uint32_t* tiles_touched,
+		bool prefiltered,
+		const float z_threshold);
+
+	// Main rasterization method.
+	void render(
+		const dim3 grid, dim3 block,
+		const uint2* ranges,
+		const uint32_t* point_list,
+		int W, int H,
+		const float2* points_xy_image,
+		const float* features,
+		const float4* conic_opacity,
+		float* final_T,
+		uint32_t* n_contrib,
+		const float* bg_color,
+		float* out_color,
+		const float* depth,
+	    float* out_depth);
+}
+
+
+#endif
\ No newline at end of file
diff --git a/src/third-party/diff-gaussian-rasterization-w-depth/cuda_rasterizer/rasterizer.h b/src/third-party/diff-gaussian-rasterization-w-depth/cuda_rasterizer/rasterizer.h
new file mode 100644
index 0000000000000000000000000000000000000000..b2a75085dc6d73d2812339f80bd40e9d5b13ef1e
--- /dev/null
+++ b/src/third-party/diff-gaussian-rasterization-w-depth/cuda_rasterizer/rasterizer.h
@@ -0,0 +1,88 @@
+/*
+ * Copyright (C) 2023, Inria
+ * GRAPHDECO research group, https://team.inria.fr/graphdeco
+ * All rights reserved.
+ *
+ * This software is free for non-commercial, research and evaluation use 
+ * under the terms of the LICENSE.md file.
+ *
+ * For inquiries contact  george.drettakis@inria.fr
+ */
+
+#ifndef CUDA_RASTERIZER_H_INCLUDED
+#define CUDA_RASTERIZER_H_INCLUDED
+
+#include <vector>
+#include <functional>
+
+namespace CudaRasterizer
+{
+	class Rasterizer
+	{
+	public:
+
+		static void markVisible(
+			int P,
+			float* means3D,
+			float* viewmatrix,
+			float* projmatrix,
+			bool* present);
+
+		static int forward(
+			std::function<char* (size_t)> geometryBuffer,
+			std::function<char* (size_t)> binningBuffer,
+			std::function<char* (size_t)> imageBuffer,
+			const int P, int D, int M,
+			const float* background,
+			const int width, int height,
+			const float* means3D,
+			const float* shs,
+			const float* colors_precomp,
+			const float* opacities,
+			const float* scales,
+			const float scale_modifier,
+			const float* rotations,
+			const float* cov3D_precomp,
+			const float* viewmatrix,
+			const float* projmatrix,
+			const float* cam_pos,
+			const float tan_fovx, float tan_fovy,
+			const bool prefiltered,
+			const float z_threshold,
+			float* out_color,
+			float* out_depth,
+			int* radii = nullptr);
+
+		static void backward(
+			const int P, int D, int M, int R,
+			const float* background,
+			const int width, int height,
+			const float* means3D,
+			const float* shs,
+			const float* colors_precomp,
+			const float* scales,
+			const float scale_modifier,
+			const float* rotations,
+			const float* cov3D_precomp,
+			const float* viewmatrix,
+			const float* projmatrix,
+			const float* campos,
+			const float tan_fovx, float tan_fovy,
+			const int* radii,
+			char* geom_buffer,
+			char* binning_buffer,
+			char* image_buffer,
+			const float* dL_dpix,
+			float* dL_dmean2D,
+			float* dL_dconic,
+			float* dL_dopacity,
+			float* dL_dcolor,
+			float* dL_dmean3D,
+			float* dL_dcov3D,
+			float* dL_dsh,
+			float* dL_dscale,
+			float* dL_drot);
+	};
+};
+
+#endif
\ No newline at end of file
diff --git a/src/third-party/diff-gaussian-rasterization-w-depth/cuda_rasterizer/rasterizer_impl.cu b/src/third-party/diff-gaussian-rasterization-w-depth/cuda_rasterizer/rasterizer_impl.cu
new file mode 100644
index 0000000000000000000000000000000000000000..2ce10abd5c9e34ed104b81cc328d23f68f0750ee
--- /dev/null
+++ b/src/third-party/diff-gaussian-rasterization-w-depth/cuda_rasterizer/rasterizer_impl.cu
@@ -0,0 +1,438 @@
+/*
+ * Copyright (C) 2023, Inria
+ * GRAPHDECO research group, https://team.inria.fr/graphdeco
+ * All rights reserved.
+ *
+ * This software is free for non-commercial, research and evaluation use 
+ * under the terms of the LICENSE.md file.
+ *
+ * For inquiries contact  george.drettakis@inria.fr
+ */
+
+#include "rasterizer_impl.h"
+#include <iostream>
+#include <fstream>
+#include <algorithm>
+#include <numeric>
+#include <cuda.h>
+#include "cuda_runtime.h"
+#include "device_launch_parameters.h"
+#include <cub/cub.cuh>
+#include <cub/device/device_radix_sort.cuh>
+#define GLM_FORCE_CUDA
+#include <glm/glm.hpp>
+
+#include <cooperative_groups.h>
+#include <cooperative_groups/reduce.h>
+namespace cg = cooperative_groups;
+
+#include "auxiliary.h"
+#include "forward.h"
+#include "backward.h"
+
+// Helper function to find the next-highest bit of the MSB
+// on the CPU.
+uint32_t getHigherMsb(uint32_t n)
+{
+	uint32_t msb = sizeof(n) * 4;
+	uint32_t step = msb;
+	while (step > 1)
+	{
+		step /= 2;
+		if (n >> msb)
+			msb += step;
+		else
+			msb -= step;
+	}
+	if (n >> msb)
+		msb++;
+	return msb;
+}
+
+// Wrapper method to call auxiliary coarse frustum containment test.
+// Mark all Gaussians that pass it.
+__global__ void checkFrustum(int P,
+	const float* orig_points,
+	const float* viewmatrix,
+	const float* projmatrix,
+	bool* present)
+{
+	auto idx = cg::this_grid().thread_rank();
+	if (idx >= P)
+		return;
+
+	float3 p_view;
+	present[idx] = in_frustum(idx, orig_points, viewmatrix, projmatrix, 0.01f, false, p_view);
+}
+
+// Generates one key/value pair for all Gaussian / tile overlaps. 
+// Run once per Gaussian (1:N mapping).
+__global__ void duplicateWithKeys(
+	int P,
+	const float2* points_xy,
+	const float* depths,
+	const uint32_t* offsets,
+	uint64_t* gaussian_keys_unsorted,
+	uint32_t* gaussian_values_unsorted,
+	int* radii,
+	dim3 grid)
+{
+	auto idx = cg::this_grid().thread_rank();
+	if (idx >= P)
+		return;
+
+	// Generate no key/value pair for invisible Gaussians
+	if (radii[idx] > 0)
+	{
+		// Find this Gaussian's offset in buffer for writing keys/values.
+		uint32_t off = (idx == 0) ? 0 : offsets[idx - 1];
+		uint2 rect_min, rect_max;
+
+		getRect(points_xy[idx], radii[idx], rect_min, rect_max, grid);
+
+		// For each tile that the bounding rect overlaps, emit a 
+		// key/value pair. The key is |  tile ID  |      depth      |,
+		// and the value is the ID of the Gaussian. Sorting the values 
+		// with this key yields Gaussian IDs in a list, such that they
+		// are first sorted by tile and then by depth. 
+		for (int y = rect_min.y; y < rect_max.y; y++)
+		{
+			for (int x = rect_min.x; x < rect_max.x; x++)
+			{
+				uint64_t key = y * grid.x + x;
+				key <<= 32;
+				key |= *((uint32_t*)&depths[idx]);
+				gaussian_keys_unsorted[off] = key;
+				gaussian_values_unsorted[off] = idx;
+				off++;
+			}
+		}
+	}
+}
+
+// Check keys to see if it is at the start/end of one tile's range in 
+// the full sorted list. If yes, write start/end of this tile. 
+// Run once per instanced (duplicated) Gaussian ID.
+__global__ void identifyTileRanges(int L, uint64_t* point_list_keys, uint2* ranges)
+{
+	auto idx = cg::this_grid().thread_rank();
+	if (idx >= L)
+		return;
+
+	// Read tile ID from key. Update start/end of tile range if at limit.
+	uint64_t key = point_list_keys[idx];
+	uint32_t currtile = key >> 32;
+	if (idx == 0)
+		ranges[currtile].x = 0;
+	else
+	{
+		uint32_t prevtile = point_list_keys[idx - 1] >> 32;
+		if (currtile != prevtile)
+		{
+			ranges[prevtile].y = idx;
+			ranges[currtile].x = idx;
+		}
+	}
+	if (idx == L - 1)
+		ranges[currtile].y = L;
+}
+
+// Mark Gaussians as visible/invisible, based on view frustum testing
+void CudaRasterizer::Rasterizer::markVisible(
+	int P,
+	float* means3D,
+	float* viewmatrix,
+	float* projmatrix,
+	bool* present)
+{
+	checkFrustum << <(P + 255) / 256, 256 >> > (
+		P,
+		means3D,
+		viewmatrix, projmatrix,
+		present);
+}
+
+CudaRasterizer::GeometryState CudaRasterizer::GeometryState::fromChunk(char*& chunk, size_t P)
+{
+	GeometryState geom;
+	obtain(chunk, geom.depths, P, 128);
+	obtain(chunk, geom.clamped, P * 3, 128);
+	obtain(chunk, geom.internal_radii, P, 128);
+	obtain(chunk, geom.means2D, P, 128);
+	obtain(chunk, geom.cov3D, P * 6, 128);
+	obtain(chunk, geom.conic_opacity, P, 128);
+	obtain(chunk, geom.rgb, P * 3, 128);
+	obtain(chunk, geom.tiles_touched, P, 128);
+	cub::DeviceScan::InclusiveSum(nullptr, geom.scan_size, geom.tiles_touched, geom.tiles_touched, P);
+	obtain(chunk, geom.scanning_space, geom.scan_size, 128);
+	obtain(chunk, geom.point_offsets, P, 128);
+	return geom;
+}
+
+CudaRasterizer::ImageState CudaRasterizer::ImageState::fromChunk(char*& chunk, size_t N)
+{
+	ImageState img;
+	obtain(chunk, img.accum_alpha, N, 128);
+	obtain(chunk, img.n_contrib, N, 128);
+	obtain(chunk, img.ranges, N, 128);
+	return img;
+}
+
+CudaRasterizer::BinningState CudaRasterizer::BinningState::fromChunk(char*& chunk, size_t P)
+{
+	BinningState binning;
+	obtain(chunk, binning.point_list, P, 128);
+	obtain(chunk, binning.point_list_unsorted, P, 128);
+	obtain(chunk, binning.point_list_keys, P, 128);
+	obtain(chunk, binning.point_list_keys_unsorted, P, 128);
+	cub::DeviceRadixSort::SortPairs(
+		nullptr, binning.sorting_size,
+		binning.point_list_keys_unsorted, binning.point_list_keys,
+		binning.point_list_unsorted, binning.point_list, P);
+	obtain(chunk, binning.list_sorting_space, binning.sorting_size, 128);
+	return binning;
+}
+
+// Forward rendering procedure for differentiable rasterization
+// of Gaussians.
+int CudaRasterizer::Rasterizer::forward(
+	std::function<char* (size_t)> geometryBuffer,
+	std::function<char* (size_t)> binningBuffer,
+	std::function<char* (size_t)> imageBuffer,
+	const int P, int D, int M,
+	const float* background,
+	const int width, int height,
+	const float* means3D,
+	const float* shs,
+	const float* colors_precomp,
+	const float* opacities,
+	const float* scales,
+	const float scale_modifier,
+	const float* rotations,
+	const float* cov3D_precomp,
+	const float* viewmatrix,
+	const float* projmatrix,
+	const float* cam_pos,
+	const float tan_fovx, float tan_fovy,
+	const bool prefiltered,
+	const float z_threshold,
+	float* out_color,
+	float* out_depth,
+	int* radii)
+{
+	const float focal_y = height / (2.0f * tan_fovy);
+	const float focal_x = width / (2.0f * tan_fovx);
+
+	size_t chunk_size = required<GeometryState>(P);
+	char* chunkptr = geometryBuffer(chunk_size);
+	GeometryState geomState = GeometryState::fromChunk(chunkptr, P);
+
+	if (radii == nullptr)
+	{
+		radii = geomState.internal_radii;
+	}
+
+	dim3 tile_grid((width + BLOCK_X - 1) / BLOCK_X, (height + BLOCK_Y - 1) / BLOCK_Y, 1);
+	dim3 block(BLOCK_X, BLOCK_Y, 1);
+
+	// Dynamically resize image-based auxiliary buffers during training
+	int img_chunk_size = required<ImageState>(width * height);
+	char* img_chunkptr = imageBuffer(img_chunk_size);
+	ImageState imgState = ImageState::fromChunk(img_chunkptr, width * height);
+
+	if (NUM_CHANNELS != 3 && colors_precomp == nullptr)
+	{
+		throw std::runtime_error("For non-RGB, provide precomputed Gaussian colors!");
+	}
+
+	// Run preprocessing per-Gaussian (transformation, bounding, conversion of SHs to RGB)
+	FORWARD::preprocess(
+		P, D, M,
+		means3D,
+		(glm::vec3*)scales,
+		scale_modifier,
+		(glm::vec4*)rotations,
+		opacities,
+		shs,
+		geomState.clamped,
+		cov3D_precomp,
+		colors_precomp,
+		viewmatrix, projmatrix,
+		(glm::vec3*)cam_pos,
+		width, height,
+		focal_x, focal_y,
+		tan_fovx, tan_fovy,
+		radii,
+		geomState.means2D,
+		geomState.depths,
+		geomState.cov3D,
+		geomState.rgb,
+		geomState.conic_opacity,
+		tile_grid,
+		geomState.tiles_touched,
+		prefiltered,
+		z_threshold
+	);
+
+	// Compute prefix sum over full list of touched tile counts by Gaussians
+	// E.g., [2, 3, 0, 2, 1] -> [2, 5, 5, 7, 8]
+	cub::DeviceScan::InclusiveSum(geomState.scanning_space, geomState.scan_size,
+		geomState.tiles_touched, geomState.point_offsets, P);
+
+	// Retrieve total number of Gaussian instances to launch and resize aux buffers
+	int num_rendered;
+	cudaMemcpy(&num_rendered, geomState.point_offsets + P - 1, sizeof(int), cudaMemcpyDeviceToHost);
+
+	int binning_chunk_size = required<BinningState>(num_rendered);
+	char* binning_chunkptr = binningBuffer(binning_chunk_size);
+	BinningState binningState = BinningState::fromChunk(binning_chunkptr, num_rendered);
+
+	// For each instance to be rendered, produce adequate [ tile | depth ] key 
+	// and corresponding dublicated Gaussian indices to be sorted
+	duplicateWithKeys << <(P + 255) / 256, 256 >> > (
+		P,
+		geomState.means2D,
+		geomState.depths,
+		geomState.point_offsets,
+		binningState.point_list_keys_unsorted,
+		binningState.point_list_unsorted,
+		radii,
+		tile_grid
+		);
+
+	int bit = getHigherMsb(tile_grid.x * tile_grid.y);
+
+	// Sort complete list of (duplicated) Gaussian indices by keys
+	cub::DeviceRadixSort::SortPairs(
+		binningState.list_sorting_space,
+		binningState.sorting_size,
+		binningState.point_list_keys_unsorted, binningState.point_list_keys,
+		binningState.point_list_unsorted, binningState.point_list,
+		num_rendered, 0, 32 + bit);
+
+	cudaMemset(imgState.ranges, 0, tile_grid.x * tile_grid.y * sizeof(uint2));
+
+	// Identify start and end of per-tile workloads in sorted list
+	if (num_rendered > 0)
+		identifyTileRanges << <(num_rendered + 255) / 256, 256 >> > (
+			num_rendered,
+			binningState.point_list_keys,
+			imgState.ranges
+			);
+
+	// Let each tile blend its range of Gaussians independently in parallel
+	const float* feature_ptr = colors_precomp != nullptr ? colors_precomp : geomState.rgb;
+	FORWARD::render(
+		tile_grid, block,
+		imgState.ranges,
+		binningState.point_list,
+		width, height,
+		geomState.means2D,
+		feature_ptr,
+		geomState.conic_opacity,
+		imgState.accum_alpha,
+		imgState.n_contrib,
+		background,
+		out_color,
+		geomState.depths,
+		out_depth);
+
+	return num_rendered;
+}
+
+// Produce necessary gradients for optimization, corresponding
+// to forward render pass
+void CudaRasterizer::Rasterizer::backward(
+	const int P, int D, int M, int R,
+	const float* background,
+	const int width, int height,
+	const float* means3D,
+	const float* shs,
+	const float* colors_precomp,
+	const float* scales,
+	const float scale_modifier,
+	const float* rotations,
+	const float* cov3D_precomp,
+	const float* viewmatrix,
+	const float* projmatrix,
+	const float* campos,
+	const float tan_fovx, float tan_fovy,
+	const int* radii,
+	char* geom_buffer,
+	char* binning_buffer,
+	char* img_buffer,
+	const float* dL_dpix,
+	float* dL_dmean2D,
+	float* dL_dconic,
+	float* dL_dopacity,
+	float* dL_dcolor,
+	float* dL_dmean3D,
+	float* dL_dcov3D,
+	float* dL_dsh,
+	float* dL_dscale,
+	float* dL_drot)
+{
+	GeometryState geomState = GeometryState::fromChunk(geom_buffer, P);
+	BinningState binningState = BinningState::fromChunk(binning_buffer, R);
+	ImageState imgState = ImageState::fromChunk(img_buffer, width * height);
+
+	if (radii == nullptr)
+	{
+		radii = geomState.internal_radii;
+	}
+
+	const float focal_y = height / (2.0f * tan_fovy);
+	const float focal_x = width / (2.0f * tan_fovx);
+
+	const dim3 tile_grid((width + BLOCK_X - 1) / BLOCK_X, (height + BLOCK_Y - 1) / BLOCK_Y, 1);
+	const dim3 block(BLOCK_X, BLOCK_Y, 1);
+
+	// Compute loss gradients w.r.t. 2D mean position, conic matrix,
+	// opacity and RGB of Gaussians from per-pixel loss gradients.
+	// If we were given precomputed colors and not SHs, use them.
+	const float* color_ptr = (colors_precomp != nullptr) ? colors_precomp : geomState.rgb;
+	BACKWARD::render(
+		tile_grid,
+		block,
+		imgState.ranges,
+		binningState.point_list,
+		width, height,
+		background,
+		geomState.means2D,
+		geomState.conic_opacity,
+		color_ptr,
+		imgState.accum_alpha,
+		imgState.n_contrib,
+		dL_dpix,
+		(float3*)dL_dmean2D,
+		(float4*)dL_dconic,
+		dL_dopacity,
+		dL_dcolor);
+
+	// Take care of the rest of preprocessing. Was the precomputed covariance
+	// given to us or a scales/rot pair? If precomputed, pass that. If not,
+	// use the one we computed ourselves.
+	const float* cov3D_ptr = (cov3D_precomp != nullptr) ? cov3D_precomp : geomState.cov3D;
+	BACKWARD::preprocess(P, D, M,
+		(float3*)means3D,
+		radii,
+		shs,
+		geomState.clamped,
+		(glm::vec3*)scales,
+		(glm::vec4*)rotations,
+		scale_modifier,
+		cov3D_ptr,
+		viewmatrix,
+		projmatrix,
+		focal_x, focal_y,
+		tan_fovx, tan_fovy,
+		(glm::vec3*)campos,
+		(float3*)dL_dmean2D,
+		dL_dconic,
+		(glm::vec3*)dL_dmean3D,
+		dL_dcolor,
+		dL_dcov3D,
+		dL_dsh,
+		(glm::vec3*)dL_dscale,
+		(glm::vec4*)dL_drot);
+}
\ No newline at end of file
diff --git a/src/third-party/diff-gaussian-rasterization-w-depth/cuda_rasterizer/rasterizer_impl.h b/src/third-party/diff-gaussian-rasterization-w-depth/cuda_rasterizer/rasterizer_impl.h
new file mode 100644
index 0000000000000000000000000000000000000000..abb23f4e7a4aef67f57a2152fdf3c13dc0d1438e
--- /dev/null
+++ b/src/third-party/diff-gaussian-rasterization-w-depth/cuda_rasterizer/rasterizer_impl.h
@@ -0,0 +1,75 @@
+/*
+ * Copyright (C) 2023, Inria
+ * GRAPHDECO research group, https://team.inria.fr/graphdeco
+ * All rights reserved.
+ *
+ * This software is free for non-commercial, research and evaluation use 
+ * under the terms of the LICENSE.md file.
+ *
+ * For inquiries contact  george.drettakis@inria.fr
+ */
+
+#pragma once
+
+#include <iostream>
+#include <vector>
+#include "rasterizer.h"
+#include <cuda_runtime_api.h>
+#include <cstdint>
+
+namespace CudaRasterizer
+{
+	template <typename T>
+	static void obtain(char*& chunk, T*& ptr, std::size_t count, std::size_t alignment)
+	{
+		std::size_t offset = (reinterpret_cast<std::uintptr_t>(chunk) + alignment - 1) & ~(alignment - 1);
+		ptr = reinterpret_cast<T*>(offset);
+		chunk = reinterpret_cast<char*>(ptr + count);
+	}
+
+	struct GeometryState
+	{
+		size_t scan_size;
+		float* depths;
+		char* scanning_space;
+		bool* clamped;
+		int* internal_radii;
+		float2* means2D;
+		float* cov3D;
+		float4* conic_opacity;
+		float* rgb;
+		uint32_t* point_offsets;
+		uint32_t* tiles_touched;
+
+		static GeometryState fromChunk(char*& chunk, size_t P);
+	};
+
+	struct ImageState
+	{
+		uint2* ranges;
+		uint32_t* n_contrib;
+		float* accum_alpha;
+
+		static ImageState fromChunk(char*& chunk, size_t N);
+	};
+
+	struct BinningState
+	{
+		size_t sorting_size;
+		uint64_t* point_list_keys_unsorted;
+		uint64_t* point_list_keys;
+		uint32_t* point_list_unsorted;
+		uint32_t* point_list;
+		char* list_sorting_space;
+
+		static BinningState fromChunk(char*& chunk, size_t P);
+	};
+
+	template<typename T> 
+	size_t required(size_t P)
+	{
+		char* size = nullptr;
+		T::fromChunk(size, P);
+		return ((size_t)size) + 128;
+	}
+};
\ No newline at end of file
diff --git a/src/third-party/diff-gaussian-rasterization-w-depth/diff_gaussian_rasterization/__init__.py b/src/third-party/diff-gaussian-rasterization-w-depth/diff_gaussian_rasterization/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..742b2da4f3f981662b92f95e9c101b9235cf7d98
--- /dev/null
+++ b/src/third-party/diff-gaussian-rasterization-w-depth/diff_gaussian_rasterization/__init__.py
@@ -0,0 +1,199 @@
+#
+# Copyright (C) 2023, Inria
+# GRAPHDECO research group, https://team.inria.fr/graphdeco
+# All rights reserved.
+#
+# This software is free for non-commercial, research and evaluation use 
+# under the terms of the LICENSE.md file.
+#
+# For inquiries contact  george.drettakis@inria.fr
+#
+
+from typing import NamedTuple
+import torch.nn as nn
+import torch
+from . import _C
+
+def rasterize_gaussians(
+    means3D,
+    means2D,
+    sh,
+    colors_precomp,
+    opacities,
+    scales,
+    rotations,
+    cov3Ds_precomp,
+    raster_settings,
+):
+    return _RasterizeGaussians.apply(
+        means3D,
+        means2D,
+        sh,
+        colors_precomp,
+        opacities,
+        scales,
+        rotations,
+        cov3Ds_precomp,
+        raster_settings,
+    )
+
+class _RasterizeGaussians(torch.autograd.Function):
+    @staticmethod
+    def forward(
+        ctx,
+        means3D,
+        means2D,
+        sh,
+        colors_precomp,
+        opacities,
+        scales,
+        rotations,
+        cov3Ds_precomp,
+        raster_settings,
+    ):
+
+        # Restructure arguments the way that the C++ lib expects them
+        args = (
+            raster_settings.bg, 
+            means3D,
+            colors_precomp,
+            opacities,
+            scales,
+            rotations,
+            raster_settings.scale_modifier,
+            cov3Ds_precomp,
+            raster_settings.viewmatrix,
+            raster_settings.projmatrix,
+            raster_settings.tanfovx,
+            raster_settings.tanfovy,
+            raster_settings.image_height,
+            raster_settings.image_width,
+            sh,
+            raster_settings.sh_degree,
+            raster_settings.campos,
+            raster_settings.prefiltered,
+            raster_settings.z_threshold,
+        )
+
+        # Invoke C++/CUDA rasterizer
+        # num_rendered, color, radii, geomBuffer, binningBuffer, imgBuffer = _C.rasterize_gaussians(*args)
+        num_rendered, color, radii, geomBuffer, binningBuffer, imgBuffer, depth = _C.rasterize_gaussians(*args)
+
+        # Keep relevant tensors for backward
+        ctx.raster_settings = raster_settings
+        ctx.num_rendered = num_rendered
+        ctx.save_for_backward(colors_precomp, means3D, scales, rotations, cov3Ds_precomp, radii, sh, geomBuffer, binningBuffer, imgBuffer)
+        return color, radii, depth
+
+    @staticmethod
+    def backward(ctx, grad_out_color, _, depth):
+
+        # Restore necessary values from context
+        num_rendered = ctx.num_rendered
+        raster_settings = ctx.raster_settings
+        colors_precomp, means3D, scales, rotations, cov3Ds_precomp, radii, sh, geomBuffer, binningBuffer, imgBuffer = ctx.saved_tensors
+
+        # Restructure args as C++ method expects them
+        args = (raster_settings.bg,
+                means3D, 
+                radii, 
+                colors_precomp, 
+                scales, 
+                rotations, 
+                raster_settings.scale_modifier, 
+                cov3Ds_precomp, 
+                raster_settings.viewmatrix, 
+                raster_settings.projmatrix, 
+                raster_settings.tanfovx, 
+                raster_settings.tanfovy, 
+                grad_out_color, 
+                sh, 
+                raster_settings.sh_degree, 
+                raster_settings.campos,
+                geomBuffer,
+                num_rendered,
+                binningBuffer,
+                imgBuffer)
+
+        # Compute gradients for relevant tensors by invoking backward method
+        grad_means2D, grad_colors_precomp, grad_opacities, grad_means3D, grad_cov3Ds_precomp, grad_sh, grad_scales, grad_rotations = _C.rasterize_gaussians_backward(*args)        
+
+        grads = (
+            grad_means3D,
+            grad_means2D,
+            grad_sh,
+            grad_colors_precomp,
+            grad_opacities,
+            grad_scales,
+            grad_rotations,
+            grad_cov3Ds_precomp,
+            None,
+        )
+
+        return grads
+
+class GaussianRasterizationSettings(NamedTuple):
+    image_height: int
+    image_width: int 
+    tanfovx : float
+    tanfovy : float
+    bg : torch.Tensor
+    scale_modifier : float
+    viewmatrix : torch.Tensor
+    projmatrix : torch.Tensor
+    sh_degree : int
+    campos : torch.Tensor
+    prefiltered : bool
+    z_threshold : float
+
+class GaussianRasterizer(nn.Module):
+    def __init__(self, raster_settings):
+        super().__init__()
+        self.raster_settings = raster_settings
+
+    def markVisible(self, positions):
+        # Mark visible points (based on frustum culling for camera) with a boolean 
+        with torch.no_grad():
+            raster_settings = self.raster_settings
+            visible = _C.mark_visible(
+                positions,
+                raster_settings.viewmatrix,
+                raster_settings.projmatrix)
+            
+        return visible
+
+    def forward(self, means3D, means2D, opacities, shs = None, colors_precomp = None, scales = None, rotations = None, cov3D_precomp = None):
+        
+        raster_settings = self.raster_settings
+
+        if (shs is None and colors_precomp is None) or (shs is not None and colors_precomp is not None):
+            raise Exception('Please provide excatly one of either SHs or precomputed colors!')
+        
+        if ((scales is None or rotations is None) and cov3D_precomp is None) or ((scales is not None or rotations is not None) and cov3D_precomp is not None):
+            raise Exception('Please provide exactly one of either scale/rotation pair or precomputed 3D covariance!')
+        
+        if shs is None:
+            shs = torch.Tensor([])
+        if colors_precomp is None:
+            colors_precomp = torch.Tensor([])
+
+        if scales is None:
+            scales = torch.Tensor([])
+        if rotations is None:
+            rotations = torch.Tensor([])
+        if cov3D_precomp is None:
+            cov3D_precomp = torch.Tensor([])
+
+        # Invoke C++/CUDA rasterization routine
+        return rasterize_gaussians(
+            means3D,
+            means2D,
+            shs,
+            colors_precomp,
+            opacities,
+            scales, 
+            rotations,
+            cov3D_precomp,
+            raster_settings, 
+        )
+
diff --git a/src/third-party/diff-gaussian-rasterization-w-depth/ext.cpp b/src/third-party/diff-gaussian-rasterization-w-depth/ext.cpp
new file mode 100644
index 0000000000000000000000000000000000000000..d768779579761238347972a973fbd1603d44235e
--- /dev/null
+++ b/src/third-party/diff-gaussian-rasterization-w-depth/ext.cpp
@@ -0,0 +1,19 @@
+/*
+ * Copyright (C) 2023, Inria
+ * GRAPHDECO research group, https://team.inria.fr/graphdeco
+ * All rights reserved.
+ *
+ * This software is free for non-commercial, research and evaluation use 
+ * under the terms of the LICENSE.md file.
+ *
+ * For inquiries contact  george.drettakis@inria.fr
+ */
+
+#include <torch/extension.h>
+#include "rasterize_points.h"
+
+PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
+  m.def("rasterize_gaussians", &RasterizeGaussiansCUDA);
+  m.def("rasterize_gaussians_backward", &RasterizeGaussiansBackwardCUDA);
+  m.def("mark_visible", &markVisible);
+}
\ No newline at end of file
diff --git a/src/third-party/diff-gaussian-rasterization-w-depth/rasterize_points.cu b/src/third-party/diff-gaussian-rasterization-w-depth/rasterize_points.cu
new file mode 100644
index 0000000000000000000000000000000000000000..e0a4dd72e86478e95cd0284ccfa360e24b9bfb00
--- /dev/null
+++ b/src/third-party/diff-gaussian-rasterization-w-depth/rasterize_points.cu
@@ -0,0 +1,217 @@
+/*
+ * Copyright (C) 2023, Inria
+ * GRAPHDECO research group, https://team.inria.fr/graphdeco
+ * All rights reserved.
+ *
+ * This software is free for non-commercial, research and evaluation use 
+ * under the terms of the LICENSE.md file.
+ *
+ * For inquiries contact  george.drettakis@inria.fr
+ */
+
+#include <math.h>
+#include <torch/extension.h>
+#include <cstdio>
+#include <sstream>
+#include <iostream>
+#include <tuple>
+#include <stdio.h>
+#include <cuda_runtime_api.h>
+#include <memory>
+#include "cuda_rasterizer/config.h"
+#include "cuda_rasterizer/rasterizer.h"
+#include <fstream>
+#include <string>
+#include <functional>
+
+std::function<char*(size_t N)> resizeFunctional(torch::Tensor& t) {
+    auto lambda = [&t](size_t N) {
+        t.resize_({(long long)N});
+		return reinterpret_cast<char*>(t.contiguous().data_ptr());
+    };
+    return lambda;
+}
+
+std::tuple<int, torch::Tensor, torch::Tensor, torch::Tensor, torch::Tensor, torch::Tensor, torch::Tensor>
+RasterizeGaussiansCUDA(
+	const torch::Tensor& background,
+	const torch::Tensor& means3D,
+    const torch::Tensor& colors,
+    const torch::Tensor& opacity,
+	const torch::Tensor& scales,
+	const torch::Tensor& rotations,
+	const float scale_modifier,
+	const torch::Tensor& cov3D_precomp,
+	const torch::Tensor& viewmatrix,
+	const torch::Tensor& projmatrix,
+	const float tan_fovx, 
+	const float tan_fovy,
+    const int image_height,
+    const int image_width,
+	const torch::Tensor& sh,
+	const int degree,
+	const torch::Tensor& campos,
+	const bool prefiltered,
+	const float z_threshold)
+{
+  if (means3D.ndimension() != 2 || means3D.size(1) != 3) {
+    AT_ERROR("means3D must have dimensions (num_points, 3)");
+  }
+  
+  const int P = means3D.size(0);
+  const int H = image_height;
+  const int W = image_width;
+
+  auto int_opts = means3D.options().dtype(torch::kInt32);
+  auto float_opts = means3D.options().dtype(torch::kFloat32);
+
+  torch::Tensor out_color = torch::full({NUM_CHANNELS, H, W}, 0.0, float_opts);
+  torch::Tensor radii = torch::full({P}, 0, means3D.options().dtype(torch::kInt32));
+  torch::Tensor out_depth = torch::full({1, H, W}, 0.0, float_opts);
+  
+  torch::Device device(torch::kCUDA);
+  torch::TensorOptions options(torch::kByte);
+  torch::Tensor geomBuffer = torch::empty({0}, options.device(device));
+  torch::Tensor binningBuffer = torch::empty({0}, options.device(device));
+  torch::Tensor imgBuffer = torch::empty({0}, options.device(device));
+  std::function<char*(size_t)> geomFunc = resizeFunctional(geomBuffer);
+  std::function<char*(size_t)> binningFunc = resizeFunctional(binningBuffer);
+  std::function<char*(size_t)> imgFunc = resizeFunctional(imgBuffer);
+  
+  int rendered = 0;
+  if(P != 0)
+  {
+	  int M = 0;
+	  if(sh.size(0) != 0)
+	  {
+		M = sh.size(1);
+      }
+
+	  rendered = CudaRasterizer::Rasterizer::forward(
+	    geomFunc,
+		binningFunc,
+		imgFunc,
+	    P, degree, M,
+		background.contiguous().data<float>(),
+		W, H,
+		means3D.contiguous().data<float>(),
+		sh.contiguous().data_ptr<float>(),
+		colors.contiguous().data<float>(), 
+		opacity.contiguous().data<float>(), 
+		scales.contiguous().data_ptr<float>(),
+		scale_modifier,
+		rotations.contiguous().data_ptr<float>(),
+		cov3D_precomp.contiguous().data<float>(), 
+		viewmatrix.contiguous().data<float>(), 
+		projmatrix.contiguous().data<float>(),
+		campos.contiguous().data<float>(),
+		tan_fovx,
+		tan_fovy,
+		prefiltered,
+		z_threshold,
+		out_color.contiguous().data<float>(),
+		out_depth.contiguous().data<float>(),
+		radii.contiguous().data<int>());
+  }
+  return std::make_tuple(rendered, out_color, radii, geomBuffer, binningBuffer, imgBuffer, out_depth);
+}
+
+std::tuple<torch::Tensor, torch::Tensor, torch::Tensor, torch::Tensor, torch::Tensor, torch::Tensor, torch::Tensor, torch::Tensor>
+ RasterizeGaussiansBackwardCUDA(
+ 	const torch::Tensor& background,
+	const torch::Tensor& means3D,
+	const torch::Tensor& radii,
+    const torch::Tensor& colors,
+	const torch::Tensor& scales,
+	const torch::Tensor& rotations,
+	const float scale_modifier,
+	const torch::Tensor& cov3D_precomp,
+	const torch::Tensor& viewmatrix,
+    const torch::Tensor& projmatrix,
+	const float tan_fovx,
+	const float tan_fovy,
+    const torch::Tensor& dL_dout_color,
+	const torch::Tensor& sh,
+	const int degree,
+	const torch::Tensor& campos,
+	const torch::Tensor& geomBuffer,
+	const int R,
+	const torch::Tensor& binningBuffer,
+	const torch::Tensor& imageBuffer) 
+{
+  const int P = means3D.size(0);
+  const int H = dL_dout_color.size(1);
+  const int W = dL_dout_color.size(2);
+  
+  int M = 0;
+  if(sh.size(0) != 0)
+  {	
+	M = sh.size(1);
+  }
+
+  torch::Tensor dL_dmeans3D = torch::zeros({P, 3}, means3D.options());
+  torch::Tensor dL_dmeans2D = torch::zeros({P, 3}, means3D.options());
+  torch::Tensor dL_dcolors = torch::zeros({P, NUM_CHANNELS}, means3D.options());
+  torch::Tensor dL_dconic = torch::zeros({P, 2, 2}, means3D.options());
+  torch::Tensor dL_dopacity = torch::zeros({P, 1}, means3D.options());
+  torch::Tensor dL_dcov3D = torch::zeros({P, 6}, means3D.options());
+  torch::Tensor dL_dsh = torch::zeros({P, M, 3}, means3D.options());
+  torch::Tensor dL_dscales = torch::zeros({P, 3}, means3D.options());
+  torch::Tensor dL_drotations = torch::zeros({P, 4}, means3D.options());
+  
+  if(P != 0)
+  {  
+	  CudaRasterizer::Rasterizer::backward(P, degree, M, R,
+	  background.contiguous().data<float>(),
+	  W, H, 
+	  means3D.contiguous().data<float>(),
+	  sh.contiguous().data<float>(),
+	  colors.contiguous().data<float>(),
+	  scales.data_ptr<float>(),
+	  scale_modifier,
+	  rotations.data_ptr<float>(),
+	  cov3D_precomp.contiguous().data<float>(),
+	  viewmatrix.contiguous().data<float>(),
+	  projmatrix.contiguous().data<float>(),
+	  campos.contiguous().data<float>(),
+	  tan_fovx,
+	  tan_fovy,
+	  radii.contiguous().data<int>(),
+	  reinterpret_cast<char*>(geomBuffer.contiguous().data_ptr()),
+	  reinterpret_cast<char*>(binningBuffer.contiguous().data_ptr()),
+	  reinterpret_cast<char*>(imageBuffer.contiguous().data_ptr()),
+	  dL_dout_color.contiguous().data<float>(),
+	  dL_dmeans2D.contiguous().data<float>(),
+	  dL_dconic.contiguous().data<float>(),  
+	  dL_dopacity.contiguous().data<float>(),
+	  dL_dcolors.contiguous().data<float>(),
+	  dL_dmeans3D.contiguous().data<float>(),
+	  dL_dcov3D.contiguous().data<float>(),
+	  dL_dsh.contiguous().data<float>(),
+	  dL_dscales.contiguous().data<float>(),
+	  dL_drotations.contiguous().data<float>());
+  }
+
+  return std::make_tuple(dL_dmeans2D, dL_dcolors, dL_dopacity, dL_dmeans3D, dL_dcov3D, dL_dsh, dL_dscales, dL_drotations);
+}
+
+torch::Tensor markVisible(
+		torch::Tensor& means3D,
+		torch::Tensor& viewmatrix,
+		torch::Tensor& projmatrix)
+{ 
+  const int P = means3D.size(0);
+  
+  torch::Tensor present = torch::full({P}, false, means3D.options().dtype(at::kBool));
+ 
+  if(P != 0)
+  {
+	CudaRasterizer::Rasterizer::markVisible(P,
+		means3D.contiguous().data<float>(),
+		viewmatrix.contiguous().data<float>(),
+		projmatrix.contiguous().data<float>(),
+		present.contiguous().data<bool>());
+  }
+  
+  return present;
+}
\ No newline at end of file
diff --git a/src/third-party/diff-gaussian-rasterization-w-depth/rasterize_points.h b/src/third-party/diff-gaussian-rasterization-w-depth/rasterize_points.h
new file mode 100644
index 0000000000000000000000000000000000000000..ad552d9f9dad31ca6aa0897ebccc2c85935c2b40
--- /dev/null
+++ b/src/third-party/diff-gaussian-rasterization-w-depth/rasterize_points.h
@@ -0,0 +1,66 @@
+/*
+ * Copyright (C) 2023, Inria
+ * GRAPHDECO research group, https://team.inria.fr/graphdeco
+ * All rights reserved.
+ *
+ * This software is free for non-commercial, research and evaluation use 
+ * under the terms of the LICENSE.md file.
+ *
+ * For inquiries contact  george.drettakis@inria.fr
+ */
+
+#pragma once
+#include <torch/extension.h>
+#include <cstdio>
+#include <tuple>
+#include <string>
+	
+std::tuple<int, torch::Tensor, torch::Tensor, torch::Tensor, torch::Tensor, torch::Tensor, torch::Tensor>
+RasterizeGaussiansCUDA(
+	const torch::Tensor& background,
+	const torch::Tensor& means3D,
+    const torch::Tensor& colors,
+    const torch::Tensor& opacity,
+	const torch::Tensor& scales,
+	const torch::Tensor& rotations,
+	const float scale_modifier,
+	const torch::Tensor& cov3D_precomp,
+	const torch::Tensor& viewmatrix,
+	const torch::Tensor& projmatrix,
+	const float tan_fovx, 
+	const float tan_fovy,
+    const int image_height,
+    const int image_width,
+	const torch::Tensor& sh,
+	const int degree,
+	const torch::Tensor& campos,
+	const bool prefiltered,
+	const float z_threshold);
+
+std::tuple<torch::Tensor, torch::Tensor, torch::Tensor, torch::Tensor, torch::Tensor, torch::Tensor, torch::Tensor, torch::Tensor>
+ RasterizeGaussiansBackwardCUDA(
+ 	const torch::Tensor& background,
+	const torch::Tensor& means3D,
+	const torch::Tensor& radii,
+    const torch::Tensor& colors,
+	const torch::Tensor& scales,
+	const torch::Tensor& rotations,
+	const float scale_modifier,
+	const torch::Tensor& cov3D_precomp,
+	const torch::Tensor& viewmatrix,
+    const torch::Tensor& projmatrix,
+	const float tan_fovx, 
+	const float tan_fovy,
+    const torch::Tensor& dL_dout_color,
+	const torch::Tensor& sh,
+	const int degree,
+	const torch::Tensor& campos,
+	const torch::Tensor& geomBuffer,
+	const int R,
+	const torch::Tensor& binningBuffer,
+	const torch::Tensor& imageBuffer);
+		
+torch::Tensor markVisible(
+		torch::Tensor& means3D,
+		torch::Tensor& viewmatrix,
+		torch::Tensor& projmatrix);
\ No newline at end of file
diff --git a/src/third-party/diff-gaussian-rasterization-w-depth/setup.py b/src/third-party/diff-gaussian-rasterization-w-depth/setup.py
new file mode 100644
index 0000000000000000000000000000000000000000..bb7220d2934d006ea756e35ecb0f391403b43d64
--- /dev/null
+++ b/src/third-party/diff-gaussian-rasterization-w-depth/setup.py
@@ -0,0 +1,34 @@
+#
+# Copyright (C) 2023, Inria
+# GRAPHDECO research group, https://team.inria.fr/graphdeco
+# All rights reserved.
+#
+# This software is free for non-commercial, research and evaluation use 
+# under the terms of the LICENSE.md file.
+#
+# For inquiries contact  george.drettakis@inria.fr
+#
+
+from setuptools import setup
+from torch.utils.cpp_extension import CUDAExtension, BuildExtension
+import os
+os.path.dirname(os.path.abspath(__file__))
+
+setup(
+    name="diff_gaussian_rasterization",
+    packages=['diff_gaussian_rasterization'],
+    ext_modules=[
+        CUDAExtension(
+            name="diff_gaussian_rasterization._C",
+            sources=[
+            "cuda_rasterizer/rasterizer_impl.cu",
+            "cuda_rasterizer/forward.cu",
+            "cuda_rasterizer/backward.cu",
+            "rasterize_points.cu",
+            "ext.cpp"],
+            extra_compile_args={"nvcc": ["-I" + os.path.join(os.path.dirname(os.path.abspath(__file__)), "third_party/glm/")]})
+        ],
+    cmdclass={
+        'build_ext': BuildExtension
+    }
+)