点云处理【三】(点云降采样)

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点云降采样

1. 为什么要降采样？

2.降采样算法(本数据单位为mm，通常数据单位为m，取半径之类得参数要除1000)

2.1 随机降采样

根据设置的比例系数随机删除点云，比较接近均匀采样，但不稳定。

Open3d

import numpy as np import open3d as o3d pcd = o3d.io.read_point_cloud("second_radius_cloud.pcd") print(pcd) # 输出点云点的个数 o3d.visualization.draw_geometries([pcd], window_name="原始点云", width=1024, height=768, left=50, top=50, mesh_show_back_face=True) downpcd = pcd.random_down_sample(sampling_ratio=0.5) print(downpcd) #降采样后的点云数 o3d.visualization.draw_geometries([downpcd], window_name="随机降采样", width=1024, height=768, left=50, top=50, mesh_show_back_face=True) 

PCL

#include <pcl/io/pcd_io.h> #include <pcl/point_types.h> #include <pcl/visualization/pcl_visualizer.h> #include <pcl/filters/random_sample.h> int main(int argc, char** argv) { 
     pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ>); pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_downsampled(new pcl::PointCloud<pcl::PointXYZ>); if (pcl::io::loadPCDFile<pcl::PointXYZ> ("69.pcd", *cloud) == -1){ 
     PCL_ERROR("couldn't read file"); return 0; } std::cout << "Loaded " << cloud->width * cloud->height << " data points" << std::endl; pcl::RandomSample<pcl::PointXYZ> random_sampling; random_sampling.setInputCloud(cloud); random_sampling.setSample(10000); // 设置希望得到的点数 random_sampling.filter(*cloud_downsampled); std::cout << "downsampled cloud size: " << cloud_downsampled->width * cloud_downsampled->height << std::endl; pcl::visualization::PCLVisualizer::Ptr viewer(new pcl::visualization::PCLVisualizer("3D Viewer")); viewer->setBackgroundColor(0, 0, 0); // 设置背景色 viewer->addPointCloud<pcl::PointXYZ>(cloud_downsampled, "sample cloud"); viewer->setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 1, "sample cloud"); viewer->initCameraParameters(); while (!viewer->wasStopped()) { 
     viewer->spinOnce(100); } return 0; } 

2.2 均匀降采样

就是每隔多远采集一个点，

Open3d

import numpy as np import open3d as o3d pcd = o3d.io.read_point_cloud("second_radius_cloud.pcd") print(pcd) # 输出点云点的个数 o3d.visualization.draw_geometries([pcd], window_name="原始点云", width=1024, height=768, left=50, top=50, mesh_show_back_face=True) downpcd = pcd.uniform_down_sample(6) print(downpcd) #降采样后的点云数 o3d.visualization.draw_geometries([downpcd], window_name="均匀降采样", width=1024, height=768, left=50, top=50, mesh_show_back_face=True) 

PCL

#include <pcl/io/pcd_io.h> #include <pcl/point_types.h> #include <pcl/visualization/pcl_visualizer.h> #include <pcl/filters/uniform_sampling.h> int main(int argc, char** argv) { 
     pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ>); pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_downsampled(new pcl::PointCloud<pcl::PointXYZ>); if (pcl::io::loadPCDFile<pcl::PointXYZ> ("69.pcd", *cloud) == -1){ 
     PCL_ERROR("couldn't read file"); return 0; } std::cout << "Loaded " << cloud->width * cloud->height << " data points" << std::endl; pcl::UniformSampling<pcl::PointXYZ> filter; // 创建均匀采样对象 filter.setInputCloud(cloud); // 设置待采样点云 filter.setRadiusSearch(10.0f); // 设置采样半径 filter.filter(*cloud_downsampled); // 执行均匀采样，结果保存在cloud_filtered中 std::cout << "downsampled cloud size: " << cloud_downsampled->width * cloud_downsampled->height << std::endl; pcl::visualization::PCLVisualizer::Ptr viewer(new pcl::visualization::PCLVisualizer("3D Viewer")); viewer->setBackgroundColor(0, 0, 0); // 设置背景色 viewer->addPointCloud<pcl::PointXYZ>(cloud_downsampled, "sample cloud"); viewer->setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 1, "sample cloud"); viewer->initCameraParameters(); viewer->saveScreenshot("screenshot.png"); while (!viewer->wasStopped()) { 
     viewer->spinOnce(100); } return 0; } 

2.3 体素降采样

将空间切割为均匀大小的体素网格，以非空体素的质心代替该体素内的所有点。

原点云位置使用体素降采样后会发生变化。

open3d

import numpy as np import open3d as o3d pcd = o3d.io.read_point_cloud("second_radius_cloud.pcd") print(pcd) # 输出点云点的个数 o3d.visualization.draw_geometries([pcd], window_name="原始点云", width=1024, height=768, left=50, top=50, mesh_show_back_face=True) downpcd = pcd.voxel_down_sample(voxel_size=5) print(downpcd) o3d.visualization.draw_geometries([downpcd], window_name="体素降采样", width=1024, height=768, left=50, top=50, mesh_show_back_face=True) 

pcl

#include <pcl/io/pcd_io.h> #include <pcl/point_types.h> #include <pcl/visualization/pcl_visualizer.h> #include <pcl/filters/voxel_grid.h> int main(int argc, char** argv) { 
     pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ>); pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_downsampled(new pcl::PointCloud<pcl::PointXYZ>); if (pcl::io::loadPCDFile<pcl::PointXYZ> ("69.pcd", *cloud) == -1){ 
     PCL_ERROR("couldn't read file"); return 0; } std::cout << "Loaded " << cloud->width * cloud->height << " data points" << std::endl; pcl::VoxelGrid<pcl::PointXYZ> sor; sor.setInputCloud(cloud); sor.setLeafSize(10.0f, 10.0f, 10.0f); sor.filter(*cloud_downsampled); std::cout << "downsampled cloud size: " << cloud_downsampled->width * cloud_downsampled->height << std::endl; pcl::visualization::PCLVisualizer::Ptr viewer(new pcl::visualization::PCLVisualizer("3D Viewer")); viewer->setBackgroundColor(0, 0, 0); // 设置背景色 viewer->addPointCloud<pcl::PointXYZ>(cloud_sampled, "sample cloud"); viewer->setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 1, "sample cloud"); viewer->initCameraParameters(); viewer->saveScreenshot("screenshot.png"); while (!viewer->wasStopped()) { 
     viewer->spinOnce(100); } return 0; } 

2.4 最远点降采样

import numpy as np import open3d as o3d pcd = o3d.io.read_point_cloud("second_radius_cloud.pcd") print(pcd) # 输出点云点的个数 o3d.visualization.draw_geometries([pcd], window_name="原始点云", width=1024, height=768, left=50, top=50, mesh_show_back_face=True) downpcd=pcd.farthest_point_down_sample(10000) print(downpcd) #降采样后的点云数 o3d.visualization.draw_geometries([downpcd], window_name="最远点降采样", width=1024, height=768, left=50, top=50, mesh_show_back_face=True) 

PCL

#include <pcl/io/pcd_io.h> #include <pcl/point_types.h> #include <pcl/visualization/pcl_visualizer.h> #include <pcl/common/distances.h> int main(int argc, char** argv) { 
     pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ>); pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_downsampled(new pcl::PointCloud<pcl::PointXYZ>); if (pcl::io::loadPCDFile<pcl::PointXYZ> ("69.pcd", *cloud) == -1){ 
     PCL_ERROR("couldn't read file"); return 0; } std::cout << "Loaded " << cloud->width * cloud->height << " data points" << std::endl; size_t N = cloud->size(); assert(N >= 10000); srand(time(0)); size_t seed_index = rand() % N; pcl::PointXYZ p = cloud->points[seed_index];; cloud_downsampled->push_back(p); cloud->erase(cloud->begin() + seed_index); for (size_t i = 1; i < 10000; i++) { 
     float max_distance = 0; size_t max_index = 0; for (size_t j = 0; j < cloud->size(); j++) { 
     float distance = pcl::euclideanDistance(p, cloud->points[j]); if (distance > max_distance) { 
     max_distance = distance; max_index = max_index; } } p = cloud->points[max_index]; cloud_downsampled->push_back(p); cloud->erase(cloud->begin() + max_index); } std::cout << "downsampled cloud size: " << cloud_downsampled->width * cloud_downsampled->height << std::endl; pcl::visualization::PCLVisualizer::Ptr viewer(new pcl::visualization::PCLVisualizer("3D Viewer")); viewer->setBackgroundColor(0, 0, 0); // 设置背景色 viewer->addPointCloud<pcl::PointXYZ>(cloud_downsampled, "sample cloud"); viewer->setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 1, "sample cloud"); viewer->initCameraParameters(); while (!viewer->wasStopped()) { 
     viewer->spinOnce(100); } return 0; } 

2.5 移动最小二乘法降采样

在MLS法中，需要在一组不同位置的节点附近建立拟合曲线，每个节点都有自己的一组系数用于定义该位置附近拟合曲线的形态。因此，在计算某个节点附近的拟合曲线时，只需要计算该点的该组系数值即可。

此外，每个节点的系数取值只考虑其临近采样点，且距离节点越近的采样点贡献越大，对于未置较远的点则不予考虑。

许多文章都将移动最小二乘法作为降采样方法，我觉得这只是一种平滑，所以这里给了重建代码，不进一步实验了。

PCL

#include <pcl/io/pcd_io.h> #include <pcl/point_types.h> #include <pcl/visualization/pcl_visualizer.h> #include <pcl/filters/voxel_grid.h> #include <pcl/surface/mls.h> #include <pcl/search/kdtree.h> int main(int argc, char** argv) { 
     pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ>); pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_downsampled(new pcl::PointCloud<pcl::PointXYZ>); if (pcl::io::loadPCDFile<pcl::PointXYZ> ("second_radius_cloud.pcd", *cloud) == -1){ 
     PCL_ERROR("couldn't read file"); return 0; } std::cout << "Loaded " << cloud->width * cloud->height << " data points" << std::endl; pcl::search::KdTree<pcl::PointXYZ>::Ptr tree (new pcl::search::KdTree<pcl::PointXYZ>); // 输出的PointCloud中有PointNormal类型，用来存储MLS算出的法线 pcl::PointCloud<pcl::PointNormal> mls_points; // 定义MovingLeastSquares对象并设置参数 pcl::MovingLeastSquares<pcl::PointXYZ, pcl::PointNormal> mls; mls.setComputeNormals(true); mls.setInputCloud(cloud); mls.setSearchMethod(tree); mls.setSearchRadius(30); // 曲面重建 mls.process(mls_points); //std::cout << "downsampled cloud size: " << mls_points->width * mls_points->height << std::endl; // 使用PCLVisualizer进行可视化 boost::shared_ptr<pcl::visualization::PCLVisualizer> viewer(new pcl::visualization::PCLVisualizer("MLS Cloud Viewer")); viewer->addPointCloud<pcl::PointNormal>(mls_points.makeShared(), "MLS Cloud"); viewer->setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 1, "MLS Cloud"); viewer->addPointCloudNormals<pcl::PointNormal>(mls_points.makeShared(), 1, 0.05, "normals"); // 可选：显示法线 viewer->saveScreenshot("screenshot.png"); while (!viewer->wasStopped()) { 
     viewer->spinOnce(100); } return 0; } 

2.6 法线空间采样

通过在法向量空间内均匀随机抽样，使所选点之间的法线分布尽可能大，结果表现为地物特征变化大的地方剩余点较多，变化小的地方剩余点稀少，可有效保持地物特征。

Open3d

import open3d as o3d import numpy as np def normal_space_sampling(pcd, num_bins=5, num_samples=10000): # 1. 估算法线 pcd.estimate_normals(search_param=o3d.geometry.KDTreeSearchParamHybrid(radius=10, max_nn=30)) normals = np.asarray(pcd.normals) # 2. 使用法线的x、y和z分量将法线映射到一个3D直方图或“bin”空间 bins = np.linspace(-1, 1, num_bins) normal_bins = np.digitize(normals, bins) unique_bins = np.unique(normal_bins, axis=0) sampled_indices = [] for b in unique_bins: indices = np.all(normal_bins == b, axis=1) bin_points = np.where(indices)[0] if bin_points.size > 0: sampled_indices.append(np.random.choice(bin_points)) # 如果采样点数不足，从原点云中随机选择其他点 while len(sampled_indices) < num_samples: sampled_indices.append(np.random.randint(0, len(pcd.points))) # 3. 从每个bin中选择一个点进行采样 sampled_points = np.asarray(pcd.points)[sampled_indices] sampled_pcd = o3d.geometry.PointCloud() sampled_pcd.points = o3d.utility.Vector3dVector(sampled_points) return sampled_pcd # 读取点云 pcd = o3d.io.read_point_cloud("second_radius_cloud.pcd") sampled_pcd = normal_space_sampling(pcd) o3d.visualization.draw_geometries([sampled_pcd]) 

PCL

#include <pcl/io/pcd_io.h> #include <pcl/point_types.h> #include <pcl/visualization/pcl_visualizer.h> #include <pcl/filters/normal_space.h> #include <pcl/features/normal_3d.h> int main(int argc, char** argv) { 
     pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ>); pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_downsampled(new pcl::PointCloud<pcl::PointXYZ>); if (pcl::io::loadPCDFile<pcl::PointXYZ> ("second_radius_cloud.pcd", *cloud) == -1){ 
     PCL_ERROR("couldn't read file"); return 0; } std::cout << "Loaded " << cloud->width * cloud->height << " data points" << std::endl; // 计算法线 pcl::NormalEstimation<pcl::PointXYZ, pcl::Normal> ne; ne.setInputCloud(cloud); pcl::search::KdTree<pcl::PointXYZ>::Ptr tree(new pcl::search::KdTree<pcl::PointXYZ>()); ne.setSearchMethod(tree); pcl::PointCloud<pcl::Normal>::Ptr cloud_normals(new pcl::PointCloud<pcl::Normal>); ne.setRadiusSearch(30); // 设置法线估计的半径 ne.compute(*cloud_normals); // 法线空间采样 pcl::NormalSpaceSampling<pcl::PointXYZ, pcl::Normal> nss; nss.setInputCloud(cloud); nss.setNormals(cloud_normals); nss.setBins(5, 5, 5); // 设置法线空间的bin数量 nss.setSample(cloud->size() / 10); // 例如，取原始点云大小的1/10 nss.filter(*cloud_downsampled); std::cout << "downsampled cloud size: " << cloud_downsampled->width * cloud_downsampled->height << std::endl; pcl::visualization::PCLVisualizer::Ptr viewer(new pcl::visualization::PCLVisualizer("3D Viewer")); viewer->setBackgroundColor(0, 0, 0); // 设置背景色 viewer->addPointCloud<pcl::PointXYZ>(cloud_downsampled, "sample cloud"); viewer->setPointCloudRenderingProperties(pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 1, "sample cloud"); viewer->initCameraParameters(); while (!viewer->wasStopped()) { 
     viewer->spinOnce(100); } return 0; }