Python三维网格体素化
本文主要是实现将一个网格模型体素化，实现不同分辨率的体素化效果，并且可视化输出为obj文件！
首先利用trimesh对mesh进行采样，然后根据采样点得到各个体素点的占有值。
效果
通过调整分辨率以及采样率（当分辨率变高时建议适量提高采样率）得到以下的效果！
代码
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
@author: Matthieu Zins
"""
import trimesh
import numpy as np
import os
import argparse
"""
====== Voxelize the surface of a mesh ======
"""
def create_if_needed(folder):
if not os.path.isdir(folder):
os.mkdir(folder)
parser = argparse.ArgumentParser(description='Pass object name')
parser.add_argument('input_mesh', type=str)
parser.add_argument('--output_folder', type=str, default="")
parser.add_argument('--resolution', type=int, nargs=3, default=[50, 50, 50],
help="resolution_X resolution_Y resolution_Z")
parser.add_argument('--sampling', type=int, default="100000",
help="number of points sampled on the mesh")
args = parser.parse_args()
input_mesh_filename = args.input_mesh
object_name = os.path.splitext(os.path.basename(input_mesh_filename))[0]
output_folder = args.output_folder
if len(output_folder) == 0: output_folder = object_name
RES_X, RES_Y, RES_Z = args.resolution
sample_points_count = args.sampling
create_if_needed(output_folder)
mesh = trimesh.exchange.load.load(input_mesh_filename)
# Uniform Points Sampling
pts, _ = trimesh.sample.sample_surface_even(mesh, sample_points_count )
# Save sample points
sampled_points_mesh = trimesh.Trimesh(vertices=pts)
sampled_points_mesh.export(os.path.join(output_folder, object_name + "_resampled_points.ply"))
# Adjust the grid origin and voxels size
origin = pts.min(axis=0)
dimensions = pts.max(axis=0) - pts.min(axis=0)
scales = np.divide(dimensions, np.array([RES_X-1, RES_Y-1, RES_Z-1]))
scale = np.max(scales)
# Voxelize
pts -= origin
pts /= scale
pts_int = np.round(pts).astype(int)
grid = np.zeros((RES_X, RES_Y, RES_Z), dtype=int)
gooRES_X = np.where(np.logical_and(pts_int[:, 0] >= 0, pts_int[:, 0] < RES_X))[0]
gooRES_Y = np.where(np.logical_and(pts_int[:, 1] >= 0, pts_int[:, 1] < RES_Y))[0]
gooRES_Z = np.where(np.logical_and(pts_int[:, 2] >= 0, pts_int[:, 2] < RES_Z))[0]
goods = np.intersect1d(np.intersect1d(gooRES_X, gooRES_Y), gooRES_Z)
pts_int = pts_int[goods, :]
grid[pts_int[:, 0], pts_int[:, 1], pts_int[:, 2]] = 1
# Save voxels
voxel_pts = np.array([[-0.5, 0.5, -0.5],
[0.5, 0.5, -0.5],
[0.5, 0.5, 0.5],
[-0.5, 0.5, 0.5],
[-0.5, -0.5, -0.5],
[0.5, -0.5, -0.5],
[0.5, -0.5, 0.5],
[-0.5, -0.5, 0.5]])
voxel_faces = np.array([[0, 1, 2, 3],
[1, 5, 6, 2],
[5, 4, 7, 6],
[4, 0, 3, 7],
[0, 4, 5, 1],
[7, 3, 2, 6]])
def get_voxel(i, j, k):
global voxel_pts, voxel_faces
v = np.array([i, j, k], dtype=float) * scale
v += origin
points = voxel_pts * scale + v
return points, voxel_faces.copy()
points = []
faces = []
fi = 0
for i in range(RES_X):
for j in range(RES_Y):
for k in range(RES_Z):
if grid[i, j, k]:
p, f = get_voxel(i, j, k)
points.append(p)
f += fi
faces.append(f)
fi += 8
points = np.vstack(points)
faces = np.vstack(faces)
# Write obj mesh with quad faces
with open(os.path.join(output_folder, object_name + "_voxels.obj"), "w") as fout:
for p in points:fout.write("v " + " ".join(map(str, p)) + "\n")
for f in faces+1:fout.write("f " + " ".join(map(str, f)) + "\n")
print(object_name, "done.")
运行：
Dependencies
- numpy
- trimesh
## Usage python voxelize_surface.py example/chair.obj --output_folder output --resolution 30 30 30 --sampling 10000
The optional parameters are：
- output_folder (string): folder where the result is saved
- resolution (list): the resolution of the grid
[res_x, res_y, res_z] - sampling (int): number of points sampled on the mesh surface before voxelization
- 注：输入的类型可以时obj也可以是off以及ply格式！
注意：
若出现如下情况，可将采样点数（sampling）提高！
出现此种情况的原因是采样间隔太大，而体素尺寸太小（分辨太高），所以导致在有些体素的占有值进行判断的时候出现错误。
所以也可以通过降低分辨率来改善此种情况！
Reference：
声明：本文的代码并非原创，来自GitHub中zinsmatt的Surface_Voxels一作！若有侵权请联系撤文！
https://github.com/zinsmatt/Surface_Voxels
总结
以上为个人经验，希望能给大家一个参考，也希望大家多多支持。
