-
Notifications
You must be signed in to change notification settings - Fork 31
/
mesh_extract_tetrahedra.py
executable file
·145 lines (120 loc) · 6.32 KB
/
mesh_extract_tetrahedra.py
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
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
#adopted from https://github.com/autonomousvision/gaussian-opacity-fields/blob/main/extract_mesh.py
import torch
from scene import Scene
import os
from os import makedirs
from gaussian_renderer import render, integrate
import random
from tqdm import tqdm
from argparse import ArgumentParser
from arguments import ModelParams, PipelineParams, get_combined_args
from gaussian_renderer import GaussianModel
import numpy as np
import trimesh
from tetranerf.utils.extension import cpp
from utils.tetmesh import marching_tetrahedra
@torch.no_grad()
def evaluage_alpha(points, views, gaussians, pipeline, background, kernel_size):
final_alpha = torch.ones((points.shape[0]), dtype=torch.float32, device="cuda")
with torch.no_grad():
for _, view in enumerate(tqdm(views, desc="Rendering progress")):
ret = integrate(points, view, gaussians, pipeline, background, kernel_size=kernel_size)
alpha_integrated = ret["alpha_integrated"]
final_alpha = torch.min(final_alpha, alpha_integrated)
alpha = 1 - final_alpha
return alpha
@torch.no_grad()
def evaluage_cull_alpha(points, views, masks, gaussians, pipeline, background, kernel_size):
# final_sdf = torch.zeros((points.shape[0]), dtype=torch.float32, device="cuda")
final_sdf = torch.ones((points.shape[0]), dtype=torch.float32, device="cuda")
weight = torch.zeros((points.shape[0]), dtype=torch.int32, device="cuda")
with torch.no_grad():
for cam_id, view in enumerate(tqdm(views, desc="Rendering progress")):
torch.cuda.empty_cache()
ret = integrate(points, view, gaussians, pipeline, background, kernel_size)
alpha_integrated = ret["alpha_integrated"]
point_coordinate = ret["point_coordinate"]
point_coordinate[:,0] = (point_coordinate[:,0]*2+1)/(views[cam_id].image_width-1) - 1
point_coordinate[:,1] = (point_coordinate[:,1]*2+1)/(views[cam_id].image_height-1) - 1
rendered_mask = ret["render"][7]
mask = rendered_mask[None]
if not view.gt_mask is None:
mask = mask * view.gt_mask
if not masks is None:
mask = mask * masks[cam_id]
valid_point_prob = torch.nn.functional.grid_sample(mask.type(torch.float32)[None],point_coordinate[None,None],padding_mode='zeros',align_corners=False)
valid_point_prob = valid_point_prob[0,0,0]
valid_point = valid_point_prob>0.5
final_sdf = torch.where(valid_point, torch.min(alpha_integrated,final_sdf), final_sdf)
weight = torch.where(valid_point, weight+1, weight)
final_sdf = torch.where(weight>0,0.5-final_sdf,-100)
return final_sdf
@torch.no_grad()
def marching_tetrahedra_with_binary_search(model_path, name, iteration, views, gaussians: GaussianModel, pipeline, background, kernel_size):
# generate tetra points here
points, points_scale = gaussians.get_tetra_points()
cells = cpp.triangulate(points)
mask = None
sdf = evaluage_cull_alpha(points, views, mask, gaussians, pipeline, background, kernel_size)
torch.cuda.empty_cache()
# the function marching_tetrahedra costs much memory, so we move it to cpu.
verts_list, scale_list, faces_list, _ = marching_tetrahedra(points.cpu()[None], cells.cpu().long(), sdf[None].cpu(), points_scale[None].cpu())
del points
del points_scale
del cells
end_points, end_sdf = verts_list[0]
end_scales = scale_list[0]
end_points, end_sdf, end_scales = end_points.cuda(), end_sdf.cuda(), end_scales.cuda()
faces=faces_list[0].cpu().numpy()
points = (end_points[:, 0, :] + end_points[:, 1, :]) / 2.
left_points = end_points[:, 0, :]
right_points = end_points[:, 1, :]
left_sdf = end_sdf[:, 0, :]
right_sdf = end_sdf[:, 1, :]
left_scale = end_scales[:, 0, 0]
right_scale = end_scales[:, 1, 0]
distance = torch.norm(left_points - right_points, dim=-1)
scale = left_scale + right_scale
n_binary_steps = 8
for step in range(n_binary_steps):
print("binary search in step {}".format(step))
mid_points = (left_points + right_points) / 2
mid_sdf = evaluage_cull_alpha(mid_points, views, mask, gaussians, pipeline, background, kernel_size)
mid_sdf = mid_sdf.unsqueeze(-1)
ind_low = ((mid_sdf < 0) & (left_sdf < 0)) | ((mid_sdf > 0) & (left_sdf > 0))
left_sdf[ind_low] = mid_sdf[ind_low]
right_sdf[~ind_low] = mid_sdf[~ind_low]
left_points[ind_low.flatten()] = mid_points[ind_low.flatten()]
right_points[~ind_low.flatten()] = mid_points[~ind_low.flatten()]
points = (left_points + right_points) / 2
mesh = trimesh.Trimesh(vertices=points.cpu().numpy(), faces=faces, process=False)
# filter
vertice_mask = (distance <= scale).cpu().numpy()
face_mask = vertice_mask[faces].all(axis=1)
mesh.update_vertices(vertice_mask)
mesh.update_faces(face_mask)
mesh.export(os.path.join(model_path,"recon.ply"))
def extract_mesh(dataset : ModelParams, iteration : int, pipeline : PipelineParams):
with torch.no_grad():
gaussians = GaussianModel(dataset.sh_degree)
scene = Scene(dataset, gaussians, load_iteration=iteration, shuffle=False)
gaussians.load_ply(os.path.join(dataset.model_path, "point_cloud", f"iteration_{iteration}", "point_cloud.ply"))
bg_color = [1,1,1] if dataset.white_background else [0, 0, 0]
background = torch.tensor(bg_color, dtype=torch.float32, device="cuda")
kernel_size = dataset.kernel_size
cams = scene.getTrainCameras()
marching_tetrahedra_with_binary_search(dataset.model_path, "test", iteration, cams, gaussians, pipeline, background, kernel_size)
if __name__ == "__main__":
# Set up command line argument parser
parser = ArgumentParser(description="Testing script parameters")
model = ModelParams(parser, sentinel=True)
pipeline = PipelineParams(parser)
parser.add_argument("--iteration", default=30000, type=int)
parser.add_argument("--quiet", action="store_true")
args = get_combined_args(parser)
print("Rendering " + args.model_path)
random.seed(0)
np.random.seed(0)
torch.manual_seed(0)
torch.cuda.set_device(torch.device("cuda:0"))
extract_mesh(model.extract(args), args.iteration, pipeline.extract(args))