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# Copyright (c) 2023, 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.
import argparse
import numpy as np
import torch
import nvdiffrast.torch as dr
import trimesh
import os
from util import *
import render
import loss
import imageio
import sys
sys.path.append('..')
from flexicubes import FlexiCubes
###############################################################################
# Functions adapted from https://github.com/NVlabs/nvdiffrec
###############################################################################
def lr_schedule(iter):
return max(0.0, 10**(-(iter)*0.0002)) # Exponential falloff from [1.0, 0.1] over 5k epochs.
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='flexicubes optimization')
parser.add_argument('-o', '--out_dir', type=str, default=None)
parser.add_argument('-rm', '--ref_mesh', type=str)
parser.add_argument('-i', '--iter', type=int, default=1000)
parser.add_argument('-b', '--batch', type=int, default=8)
parser.add_argument('-r', '--train_res', nargs=2, type=int, default=[2048, 2048])
parser.add_argument('-lr', '--learning_rate', type=float, default=0.01)
parser.add_argument('--voxel_grid_res', type=int, default=64)
parser.add_argument('--sdf_loss', type=bool, default=True)
parser.add_argument('--develop_reg', type=bool, default=False)
parser.add_argument('--sdf_regularizer', type=float, default=0.2)
parser.add_argument('-dr', '--display_res', nargs=2, type=int, default=[512, 512])
parser.add_argument('-si', '--save_interval', type=int, default=20)
FLAGS = parser.parse_args()
device = 'cuda'
os.makedirs(FLAGS.out_dir, exist_ok=True)
glctx = dr.RasterizeGLContext()
# Load GT mesh
gt_mesh = load_mesh(FLAGS.ref_mesh, device)
gt_mesh.auto_normals() # compute face normals for visualization
# ==============================================================================================
# Create and initialize FlexiCubes
# ==============================================================================================
fc = FlexiCubes(device)
x_nx3, cube_fx8 = fc.construct_voxel_grid(FLAGS.voxel_grid_res)
x_nx3 *= 2 # scale up the grid so that it's larger than the target object
sdf = torch.rand_like(x_nx3[:,0]) - 0.1 # randomly init SDF
sdf = torch.nn.Parameter(sdf.clone().detach(), requires_grad=True)
# set per-cube learnable weights to zeros
weight = torch.zeros((cube_fx8.shape[0], 21), dtype=torch.float, device='cuda')
weight = torch.nn.Parameter(weight.clone().detach(), requires_grad=True)
deform = torch.nn.Parameter(torch.zeros_like(x_nx3), requires_grad=True)
# Retrieve all the edges of the voxel grid; these edges will be utilized to
# compute the regularization loss in subsequent steps of the process.
all_edges = cube_fx8[:, fc.cube_edges].reshape(-1, 2)
grid_edges = torch.unique(all_edges, dim=0)
# ==============================================================================================
# Setup optimizer
# ==============================================================================================
optimizer = torch.optim.Adam([sdf, weight,deform], lr=FLAGS.learning_rate)
scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda x: lr_schedule(x))
# ==============================================================================================
# Train loop
# ==============================================================================================
for it in range(FLAGS.iter):
optimizer.zero_grad()
# sample random camera poses
mv, mvp = render.get_random_camera_batch(FLAGS.batch, iter_res=FLAGS.train_res, device=device, use_kaolin=False)
# render gt mesh
target = render.render_mesh_paper(gt_mesh, mv, mvp, FLAGS.train_res)
# extract and render FlexiCubes mesh
grid_verts = x_nx3 + (2-1e-8) / (FLAGS.voxel_grid_res * 2) * torch.tanh(deform)
vertices, faces, L_dev = fc(grid_verts, sdf, cube_fx8, FLAGS.voxel_grid_res, beta_fx12=weight[:,:12], alpha_fx8=weight[:,12:20],
gamma_f=weight[:,20], training=True)
flexicubes_mesh = Mesh(vertices, faces)
buffers = render.render_mesh_paper(flexicubes_mesh, mv, mvp, FLAGS.train_res)
# evaluate reconstruction loss
mask_loss = (buffers['mask'] - target['mask']).abs().mean()
depth_loss = (((((buffers['depth'] - (target['depth']))* target['mask'])**2).sum(-1)+1e-8)).sqrt().mean() * 10
t_iter = it / FLAGS.iter
sdf_weight = FLAGS.sdf_regularizer - (FLAGS.sdf_regularizer - FLAGS.sdf_regularizer/20)*min(1.0, 4.0 * t_iter)
reg_loss = loss.sdf_reg_loss(sdf, grid_edges).mean() * sdf_weight # Loss to eliminate internal floaters that are not visible
reg_loss += L_dev.mean() * 0.5
reg_loss += (weight[:,:20]).abs().mean() * 0.1
total_loss = mask_loss + depth_loss + reg_loss
if FLAGS.sdf_loss: # optionally add SDF loss to eliminate internal structures
with torch.no_grad():
pts = sample_random_points(1000, gt_mesh)
gt_sdf = compute_sdf(pts, gt_mesh.vertices, gt_mesh.faces)
pred_sdf = compute_sdf(pts, flexicubes_mesh.vertices, flexicubes_mesh.faces)
total_loss += torch.nn.functional.mse_loss(pred_sdf, gt_sdf) * 2e3
# optionally add developability regularizer, as described in paper section 5.2
if FLAGS.develop_reg:
reg_weight = max(0, t_iter - 0.8) * 5
if reg_weight > 0: # only applied after shape converges
reg_loss = loss.mesh_developable_reg(flexicubes_mesh).mean() * 10
reg_loss += (deform).abs().mean()
reg_loss += (weight[:,:20]).abs().mean()
total_loss = mask_loss + depth_loss + reg_loss
total_loss.backward()
optimizer.step()
scheduler.step()
if (it % FLAGS.save_interval == 0 or it == (FLAGS.iter-1)): # save normal image for visualization
with torch.no_grad():
# extract mesh with training=False
vertices, faces, L_dev = fc(grid_verts, sdf, cube_fx8, FLAGS.voxel_grid_res, beta_fx12=weight[:,:12], alpha_fx8=weight[:,12:20],
gamma_f=weight[:,20], training=False)
flexicubes_mesh = Mesh(vertices, faces)
flexicubes_mesh.auto_normals() # compute face normals for visualization
mv, mvp = render.get_rotate_camera(it//FLAGS.save_interval, iter_res=FLAGS.display_res, device=device,use_kaolin=False)
val_buffers = render.render_mesh_paper(flexicubes_mesh, mv.unsqueeze(0), mvp.unsqueeze(0), FLAGS.display_res, return_types=["normal"], white_bg=True)
val_image = ((val_buffers["normal"][0].detach().cpu().numpy()+1)/2*255).astype(np.uint8)
gt_buffers = render.render_mesh_paper(gt_mesh, mv.unsqueeze(0), mvp.unsqueeze(0), FLAGS.display_res, return_types=["normal"], white_bg=True)
gt_image = ((gt_buffers["normal"][0].detach().cpu().numpy()+1)/2*255).astype(np.uint8)
imageio.imwrite(os.path.join(FLAGS.out_dir, '{:04d}.png'.format(it)), np.concatenate([val_image, gt_image], 1))
print(f"Optimization Step [{it}/{FLAGS.iter}], Loss: {total_loss.item():.4f}")
# ==============================================================================================
# Save ouput
# ==============================================================================================
mesh_np = trimesh.Trimesh(vertices = vertices.detach().cpu().numpy(), faces=faces.detach().cpu().numpy(), process=False)
mesh_np.export(os.path.join(FLAGS.out_dir, 'output_mesh.obj')) |