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 # https://pointclouds.org/documentation/tutorials/resampling.php#moving-least-squares import numpy as np import pcl import random def 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() #
