测试环境：

pcl==1.12.1

python-pcl==0.3.1

python==3.7

代码：

# -*- coding: utf-8 -*-
# Construct a concave or convex hull polygon for a plane model
# http://pointclouds.org/documentation/tutorials/hull_2d.php#hull-2dimport numpy as np
import pcl
import randomdef main():#  pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ>),#                                      cloud_filtered (new pcl::PointCloud<pcl::PointXYZ>),#                                      cloud_projected (new pcl::PointCloud<pcl::PointXYZ>);# cloud = pcl.PointCloud()# cloud_filtered = pcl.PointCloud()# cloud_projected = pcl.PointCloud()#  pcl::PCDReader reader;#  reader.read ("table_scene_mug_stereo_textured.pcd", *cloud);cloud = pcl.load(r"C:\Users\Administrator\Desktop\python-pcl\examples\pcldata\tutorials\table_scene_mug_stereo_textured.pcd")# // Build a filter to remove spurious NaNs# pcl::PassThrough<pcl::PointXYZ> pass;# pass.setInputCloud (cloud);# pass.setFilterFieldName ("z");# pass.setFilterLimits (0, 1.1);# pass.filter (*cloud_filtered);# std::cerr << "PointCloud after filtering has: "#           << cloud_filtered->points.size () << " data points." << std::endl;passthrough = cloud.make_passthrough_filter()passthrough.set_filter_field_name("z")passthrough.set_filter_limits(0.0, 1.1)cloud_filtered = passthrough.filter()print('PointCloud after filtering has: ' +str(cloud_filtered.size) + ' data points.')# pcl::ModelCoefficients::Ptr coefficients (new pcl::ModelCoefficients);# pcl::PointIndices::Ptr inliers (new pcl::PointIndices);# // Create the segmentation object# pcl::SACSegmentation<pcl::PointXYZ> seg;# // Optional# seg.setOptimizeCoefficients (true);# // Mandatory# seg.setModelType (pcl::SACMODEL_PLANE);# seg.setMethodType (pcl::SAC_RANSAC);# seg.setDistanceThreshold (0.01);# seg.setInputCloud (cloud_filtered);# seg.segment (*inliers, *coefficients);# std::cerr << "PointCloud after segmentation has: "#           << inliers->indices.size () << " inliers." << std::endl;seg = cloud_filtered.make_segmenter_normals(ksearch=50)seg.set_optimize_coefficients(True)seg.set_model_type(pcl.SACMODEL_NORMAL_PLANE)seg.set_method_type(pcl.SAC_RANSAC)seg.set_distance_threshold(0.01)indices, model = seg.segment()print('PointCloud after segmentation has: ' +str(indices.count) + ' inliers.')#   // Project the model inliers#   pcl::ProjectInliers<pcl::PointXYZ> proj;#   proj.setModelType (pcl::SACMODEL_PLANE);#   proj.setIndices (inliers);#   proj.setInputCloud (cloud_filtered);#   proj.setModelCoefficients (coefficients);#   proj.filter (*cloud_projected);#   std::cerr << "PointCloud after projection has: "#             << cloud_projected->points.size () << " data points." << std::endl;proj = cloud_filtered.make_ProjectInliers()proj.set_model_type(pcl.SACMODEL_PLANE)#   proj.setIndices (inliers);#   proj.setModelCoefficients (coefficients)cloud_projected = proj.filter()print('PointCloud after projection has: ' +str(cloud_projected.size) + ' data points.')#   // Create a Concave Hull representation of the projected inliers#   pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_hull (new pcl::PointCloud<pcl::PointXYZ>);#   pcl::ConcaveHull<pcl::PointXYZ> chull;#   chull.setInputCloud (cloud_projected);#   chull.setAlpha (0.1);#   chull.reconstruct (*cloud_hull);#   std::cerr << "Concave hull has: " << cloud_hull->points.size ()#             << " data points." << std::endl;# cloud_projected = pcl.PointCloud()chull = cloud_projected.make_ConcaveHull()chull.set_Alpha(0.1)cloud_hull = chull.reconstruct()print('Concave hull has: ' + str(cloud_hull.size) + ' data points.')#   pcl::PCDWriter writer;#   writer.write ("table_scene_mug_stereo_textured_hull.pcd", *cloud_hull, false);if cloud_hull.size != 0:pcl.save(cloud_hull, 'table_scene_mug_stereo_textured_hull.pcd')if __name__ == "__main__":# import cProfile# cProfile.run('main()', sort='time')main()

运行结果：

PointCloud after filtering has: 139656 data points.
PointCloud after segmentation has: <built-in method count of list object at 0x0000028CA3429A48> inliers.
PointCloud after projection has: 139656 data points.
Concave hull has: 281 data points.

table_scene_mug_stereo_textured.pcd文件需要去这个地址下载：

https://github.com/strawlab/python-pcl/blob/master/examples/pcldata/tutorials/table_scene_mug_stereo_textured.pcd