测试环境：

pcl==1.12.1

python-pcl==0.3.1

python==3.7

代码：

# -*- coding: utf-8 -*-
# Smoothing and normal estimation based on polynomial reconstruction
# http://pointclouds.org/documentation/tutorials/resampling.php#moving-least-squaresimport numpy as np
import pcl
import randomdef main():# // Load input file into a PointCloud<T> with an appropriate type# pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ> ());# // Load bun0.pcd -- should be available with the PCL archive in test# pcl::io::loadPCDFile ("bun0.pcd", *cloud);cloud = pcl.load('bun0.pcd')print('cloud(size) = ' + str(cloud.size))# // Create a KD-Tree# pcl::search::KdTree<pcl::PointXYZ>::Ptr tree (new pcl::search::KdTree<pcl::PointXYZ>);tree = cloud.make_kdtree()# tree = cloud.make_kdtree_flann()# blankCloud = pcl.PointCloud()# tree = blankCloud.make_kdtree()# // Output has the PointNormal type in order to store the normals calculated by MLS# pcl::PointCloud<pcl::PointNormal> mls_points;# mls_points = pcl.PointCloudNormal()# // Init object (second point type is for the normals, even if unused)# pcl::MovingLeastSquares<pcl::PointXYZ, pcl::PointNormal> mls;# mls.setComputeNormals (true);## // Set parameters# mls.setInputCloud (cloud);# mls.setPolynomialFit (true);# mls.setSearchMethod (tree);# mls.setSearchRadius (0.03);## // Reconstruct# mls.process (mls_points);mls = cloud.make_moving_least_squares()# print('make_moving_least_squares')mls.set_Compute_Normals(True)mls.set_polynomial_fit(True)mls.set_Search_Method(tree)mls.set_search_radius(0.03)print('set parameters')mls_points = mls.process()# Save output# pcl::io::savePCDFile ("bun0-mls.pcd", mls_points);pcl.save_PointNormal(mls_points, 'bun0-mls.pcd')if __name__ == "__main__":# import cProfile# cProfile.run('main()', sort='time')main()

运行结果：

cloud(size) = 112586
set parameters

bun0.pcd文件需要去这个地址下载：https://github.com/strawlab/python-pcl/blob/master/examples/official/Surface/bun0.pcd