Spaces:
Sleeping
Sleeping
File size: 4,870 Bytes
71d5bf5 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 |
# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION & AFFILIATES and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION & AFFILIATES is strictly prohibited.
from __future__ import annotations
import nvdiffrast.torch as dr
import torch
import torch.nn.functional as F
from . import Renderer
_FG_LUT = None
def interpolate(attr, rast, attr_idx, rast_db=None):
return dr.interpolate(
attr.contiguous(), rast, attr_idx, rast_db=rast_db,
diff_attrs=None if rast_db is None else 'all')
def xfm_points(points, matrix, use_python=True):
"""
Transform points.
Args:
----
points: Tensor containing 3D points with shape [minibatch_size, num_vertices, 3] or [1, num_vertices, 3]
matrix: A 4x4 transform matrix with shape [minibatch_size, 4, 4]
use_python: Use PyTorch's torch.matmul (for validation)
Returns:
-------
Transformed points in homogeneous 4D with shape [minibatch_size, num_vertices, 4].
"""
out = torch.matmul(torch.nn.functional.pad(points, pad=(0, 1), mode='constant', value=1.0), torch.transpose(matrix, 1, 2))
if torch.is_anomaly_enabled():
assert torch.all(torch.isfinite(out)), "Output of xfm_points contains inf or NaN"
return out
def dot(x, y):
return torch.sum(x * y, -1, keepdim=True)
def compute_vertex_normal(v_pos, t_pos_idx):
i0 = t_pos_idx[:, 0]
i1 = t_pos_idx[:, 1]
i2 = t_pos_idx[:, 2]
v0 = v_pos[i0, :]
v1 = v_pos[i1, :]
v2 = v_pos[i2, :]
face_normals = torch.cross(v1 - v0, v2 - v0)
# Splat face normals to vertices
v_nrm = torch.zeros_like(v_pos)
v_nrm.scatter_add_(0, i0[:, None].repeat(1, 3), face_normals)
v_nrm.scatter_add_(0, i1[:, None].repeat(1, 3), face_normals)
v_nrm.scatter_add_(0, i2[:, None].repeat(1, 3), face_normals)
# Normalize, replace zero (degenerated) normals with some default value
v_nrm = torch.where(
dot(v_nrm, v_nrm) > 1e-20, v_nrm, torch.as_tensor([0.0, 0.0, 1.0]).to(v_nrm)
)
v_nrm = F.normalize(v_nrm, dim=1)
assert torch.all(torch.isfinite(v_nrm))
return v_nrm
class NeuralRender(Renderer):
def __init__(self, device='cuda', camera_model=None):
super().__init__()
self.device = device
self.ctx = dr.RasterizeCudaContext(device=device)
self.projection_mtx = None
self.camera = camera_model
def render_mesh(
self,
mesh_v_pos_bxnx3,
mesh_t_pos_idx_fx3,
camera_mv_bx4x4,
mesh_v_feat_bxnxd,
resolution=256,
spp=1,
device='cuda',
hierarchical_mask=False
):
assert not hierarchical_mask
mtx_in = torch.tensor(camera_mv_bx4x4, dtype=torch.float32, device=device) if not torch.is_tensor(camera_mv_bx4x4) else camera_mv_bx4x4
v_pos = xfm_points(mesh_v_pos_bxnx3, mtx_in) # Rotate it to camera coordinates
v_pos_clip = self.camera.project(v_pos) # Projection in the camera
v_nrm = compute_vertex_normal(mesh_v_pos_bxnx3[0], mesh_t_pos_idx_fx3.long()) # vertex normals in world coordinates
# Render the image,
# Here we only return the feature (3D location) at each pixel, which will be used as the input for neural render
num_layers = 1
mask_pyramid = None
assert mesh_t_pos_idx_fx3.shape[0] > 0 # Make sure we have shapes
mesh_v_feat_bxnxd = torch.cat([mesh_v_feat_bxnxd.repeat(v_pos.shape[0], 1, 1), v_pos], dim=-1) # Concatenate the pos
with dr.DepthPeeler(self.ctx, v_pos_clip, mesh_t_pos_idx_fx3, [resolution * spp, resolution * spp]) as peeler:
for _ in range(num_layers):
rast, _db = peeler.rasterize_next_layer()
gb_feat, _ = interpolate(mesh_v_feat_bxnxd, rast, mesh_t_pos_idx_fx3)
hard_mask = torch.clamp(rast[..., -1:], 0, 1)
antialias_mask = dr.antialias(
hard_mask.clone().contiguous(), rast, v_pos_clip,
mesh_t_pos_idx_fx3)
depth = gb_feat[..., -2:-1]
ori_mesh_feature = gb_feat[..., :-4]
normal, _ = interpolate(v_nrm[None, ...], rast, mesh_t_pos_idx_fx3)
normal = dr.antialias(normal.clone().contiguous(), rast, v_pos_clip, mesh_t_pos_idx_fx3)
normal = F.normalize(normal, dim=-1)
normal = torch.lerp(torch.zeros_like(normal), (normal + 1.0) / 2.0, hard_mask.float()) # black background
return ori_mesh_feature, antialias_mask, hard_mask, rast, v_pos_clip, mask_pyramid, depth, normal
|