Spaces:
Running
on
Zero
Running
on
Zero
File size: 6,469 Bytes
476e0f0 |
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)) |