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import mesh2sdf.core
import numpy as np
import skimage.measure
import trimesh
def normalize_vertices(vertices, scale=0.9):
bbmin, bbmax = vertices.min(0), vertices.max(0)
center = (bbmin + bbmax) * 0.5
scale = 2.0 * scale / (bbmax - bbmin).max()
vertices = (vertices - center) * scale
return vertices, center, scale
def export_to_watertight(normalized_mesh, octree_depth: int = 7):
"""
Convert the non-watertight mesh to watertight.
Args:
input_path (str): normalized path
octree_depth (int):
Returns:
mesh(trimesh.Trimesh): watertight mesh
"""
size = 2 ** octree_depth
level = 2 / size
scaled_vertices, to_orig_center, to_orig_scale = normalize_vertices(normalized_mesh.vertices)
sdf = mesh2sdf.core.compute(scaled_vertices, normalized_mesh.faces, size=size)
vertices, faces, normals, _ = skimage.measure.marching_cubes(np.abs(sdf), level)
# watertight mesh
vertices = vertices / size * 2 - 1 # -1 to 1
vertices = vertices / to_orig_scale + to_orig_center
# vertices = vertices / to_orig_scale + to_orig_center
mesh = trimesh.Trimesh(vertices, faces, normals=normals)
return mesh
def process_mesh_to_pc(mesh_list, marching_cubes = False, sample_num = 4096):
# mesh_list : list of trimesh
pc_normal_list = []
return_mesh_list = []
for mesh in mesh_list:
if marching_cubes:
mesh = export_to_watertight(mesh)
print("MC over!")
return_mesh_list.append(mesh)
points, face_idx = mesh.sample(sample_num, return_index=True)
normals = mesh.face_normals[face_idx]
pc_normal = np.concatenate([points, normals], axis=-1, dtype=np.float16)
pc_normal_list.append(pc_normal)
print("process mesh success")
return pc_normal_list, return_mesh_list
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