本文主要是介绍PCL点云使用贪婪三角化进行曲面重构，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
一、PCL点云平滑和法线估计
题目：https://mp.weixin.qq.com/s?__biz=MzIxOTczOTM4NA==&mid=2247486705&idx=1&sn=ca333d7bb12b7c226270e98d0003a789&chksm=97d7e966a0a06070a8dba605966016d227d7a6cad786498070d9e1b8cea8747470a4840257fd&scene=21#wechat_redirect
/***************************** 题目：给定一个融合后的点云，已经对其进行下采样和滤波（代码已给）。* 请对其进行平滑（输出结果），然后计算法线，并讲法线显示在平滑后的点云上（提供截图）。*
* 本程序学习目标：* 熟悉PCL的平滑、法线计算、显示，为网格化做铺垫。** 公众号：计算机视觉life。发布于公众号旗下知识星球：从零开始学习SLAM* 时间：2018.12
****************************/
#include <pcl/point_types.h>
#include <pcl/io/io.h>
#include <pcl/io/pcd_io.h>
#include <pcl/visualization/cloud_viewer.h>
#include <pcl/filters/radius_outlier_removal.h>
#include <pcl/filters/voxel_grid.h>
#include <pcl/filters/statistical_outlier_removal.h>
#include <pcl/surface/mls.h>
#include <pcl/features/normal_3d.h>typedef pcl::PointXYZRGB PointT;int main(int argc, char** argv)
{// Load input filepcl::PointCloud<PointT>::Ptr cloud(new pcl::PointCloud<PointT>);pcl::PointCloud<PointT>::Ptr cloud_downSampled(new pcl::PointCloud<PointT>);pcl::PointCloud<PointT>::Ptr cloud_filtered(new pcl::PointCloud<PointT>);pcl::PointCloud<PointT>::Ptr cloud_smoothed(new pcl::PointCloud<PointT>);if (pcl::io::loadPCDFile("./fusedCloud.pcd", *cloud) == -1){cout << "点云数据读取失败！" << endl;}std::cout << "Orginal points number: " << cloud->points.size() << std::endl;// 下采样，同时保持点云形状特征pcl::VoxelGrid<PointT> downSampled; //创建滤波对象downSampled.setInputCloud (cloud); //设置需要过滤的点云给滤波对象downSampled.setLeafSize (0.01f, 0.01f, 0.01f); //设置滤波时创建的体素体积为1cm的立方体downSampled.filter (*cloud_downSampled); //执行滤波处理，存储输出std::cout<<"cloud_downSampled: " << cloud_downSampled->size()<<std::endl;pcl::io::savePCDFile ("./downsampledPC.pcd", *cloud_downSampled);// 统计滤波pcl::StatisticalOutlierRemoval<PointT> statisOutlierRemoval; //创建滤波器对象statisOutlierRemoval.setInputCloud (cloud_downSampled); //设置待滤波的点云statisOutlierRemoval.setMeanK (50); //设置在进行统计时考虑查询点临近点数statisOutlierRemoval.setStddevMulThresh (3.0); //设置判断是否为离群点的阀值:均值+1.0*标准差statisOutlierRemoval.filter (*cloud_filtered); //滤波结果存储到cloud_filteredstd::cout << "cloud_filtered: " << cloud_filtered->size()<<std::endl;pcl::io::savePCDFile ("./filteredPC.pcd", *cloud_filtered);// ----------------------开始你的代码--------------------------//// 请参考PCL官网实现以下功能// 对点云重采样pcl::search::KdTree<PointT>::Ptr treeSampling (new pcl::search::KdTree<PointT>);pcl::PointCloud<PointT> mls_point;pcl::MovingLeastSquares<PointT,PointT> mls;mls.setComputeNormals(false);mls.setInputCloud(cloud_filtered);mls.setPolynomialOrder(2);mls.setPolynomialFit(false);mls.setSearchMethod(treeSampling);mls.setSearchRadius(0.05);mls.process(mls_point);// 输出重采样结果cloud_smoothed = mls_point.makeShared();std::cout<<"cloud_smoothed: "<<cloud_smoothed->size() <<std::endl;//save cloud_smoothedpcl::io::savePCDFileASCII("cloud_smoothed.pcd",*cloud_smoothed);// 法线估计pcl::NormalEstimation<PointT,pcl::Normal> normalEstimation;normalEstimation.setInputCloud(cloud_smoothed);pcl::search::KdTree<PointT>::Ptr tree(new pcl::search::KdTree<PointT>);normalEstimation.setSearchMethod(tree);pcl::PointCloud<pcl::Normal>::Ptr normals(new pcl::PointCloud<pcl::Normal>);normalEstimation.setKSearch(10);normalEstimation.compute(*normals);std::cout<<"normals: "<<normals->size()<<", "<<"normals fields: "<<pcl::getFieldsList(*normals)<<std::endl;pcl::io::savePCDFileASCII("normals.pcd",*normals);// ----------------------结束你的代码--------------------------//// 显示结果boost::shared_ptr<pcl::visualization::PCLVisualizer> viewer (new pcl::visualization::PCLVisualizer ("PCL Viewer"));viewer->setBackgroundColor (0, 0, 0);pcl::visualization::PointCloudColorHandlerRGBField<PointT> rgb(cloud_smoothed);//colorviewer->addPointCloud<PointT> (cloud_smoothed, rgb, "smooth cloud");viewer->setPointCloudRenderingProperties (pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 3, "smooth cloud");viewer->addPointCloudNormals<PointT, pcl::Normal> (cloud_smoothed, normals, 10, 0.1, "normals");viewer->initCameraParameters ();while (!viewer->wasStopped ()){viewer->spinOnce (100);boost::this_thread::sleep (boost::posix_time::microseconds (100000));}return 0;
}
1、控制法线显示的数目：
viewer->addPointCloudNormals<PointT, pcl::Normal> (cloud_smoothed, normals, 10, 0.1, "normals");10，就是每10个法线显示一个。
0.1，就是每个法线的长度。
2、获取PointCloud的Fields
pcl::getFieldsList(*normals)二、贪婪三角化投影曲面重建
计算流程：点云输入 --> 下采样 --> 统计滤波去除离群点 --> mls移动最小二乘法进行平滑处理 --> 对平滑后的点云进行法线估计(有向点云) --> 将法线和平滑后的点云的Fields拼接在一起 --> 贪婪三角化 -->显示输出
/***************************** 题目：给定一个融合后的点云，已经对其进行下采样和滤波（代码已给）。* 请对其进行平滑（输出结果），然后计算法线，并讲法线显示在平滑后的点云上（提供截图）。*
* 本程序学习目标：* 熟悉PCL的平滑、法线计算、显示，为网格化做铺垫。** 公众号：计算机视觉life。发布于公众号旗下知识星球：从零开始学习SLAM* 时间：2018.12
****************************/
#include <pcl/point_types.h>
#include <pcl/io/io.h>
#include <pcl/io/pcd_io.h>
#include <pcl/visualization/cloud_viewer.h>
#include <pcl/filters/radius_outlier_removal.h>
#include <pcl/filters/voxel_grid.h>
#include <pcl/filters/statistical_outlier_removal.h>
#include <pcl/surface/mls.h>
#include <pcl/features/normal_3d.h>#include <pcl/surface/gp3.h>typedef pcl::PointXYZ PointT;int main(int argc, char** argv)
{// Load input filepcl::PointCloud<PointT>::Ptr cloud(new pcl::PointCloud<PointT>);pcl::PointCloud<PointT>::Ptr cloud_downSampled(new pcl::PointCloud<PointT>);pcl::PointCloud<PointT>::Ptr cloud_filtered(new pcl::PointCloud<PointT>);pcl::PointCloud<PointT>::Ptr cloud_smoothed(new pcl::PointCloud<PointT>);if (pcl::io::loadPCDFile("./fusedCloud.pcd", *cloud) == -1){cout << "点云数据读取失败！" << endl;}std::cout << "Orginal points number: " << cloud->points.size() << std::endl;// 下采样，同时保持点云形状特征pcl::VoxelGrid<PointT> downSampled; //创建滤波对象downSampled.setInputCloud (cloud); //设置需要过滤的点云给滤波对象downSampled.setLeafSize (0.01f, 0.01f, 0.01f); //设置滤波时创建的体素体积为1cm的立方体downSampled.filter (*cloud_downSampled); //执行滤波处理，存储输出std::cout<<"cloud_downSampled: " << cloud_downSampled->size()<<std::endl;pcl::io::savePCDFile ("./downsampledPC.pcd", *cloud_downSampled);// 统计滤波pcl::StatisticalOutlierRemoval<PointT> statisOutlierRemoval; //创建滤波器对象statisOutlierRemoval.setInputCloud (cloud_downSampled); //设置待滤波的点云statisOutlierRemoval.setMeanK (50); //设置在进行统计时考虑查询点临近点数statisOutlierRemoval.setStddevMulThresh (3.0); //设置判断是否为离群点的阀值:均值+1.0*标准差statisOutlierRemoval.filter (*cloud_filtered); //滤波结果存储到cloud_filteredstd::cout << "cloud_statical_filtered: " << cloud_filtered->size()<<std::endl;pcl::io::savePCDFile ("./filteredPC.pcd", *cloud_filtered);// ----------------------开始你的代码--------------------------//// 请参考PCL官网实现以下功能// 对点云重采样pcl::search::KdTree<PointT>::Ptr treeSampling (new pcl::search::KdTree<PointT>);pcl::PointCloud<PointT> mls_point;pcl::MovingLeastSquares<PointT,PointT> mls;mls.setComputeNormals(false);mls.setInputCloud(cloud_filtered);mls.setPolynomialOrder(2);mls.setPolynomialFit(false);mls.setSearchMethod(treeSampling);mls.setSearchRadius(0.05);mls.process(mls_point);// 输出重采样结果cloud_smoothed = mls_point.makeShared();std::cout<<"cloud_smoothed: "<<cloud_smoothed->size() <<std::endl;//save cloud_smoothedpcl::io::savePCDFileASCII("cloud_smoothed.pcd",*cloud_smoothed);// 法线估计pcl::NormalEstimation<PointT,pcl::Normal> normalEstimation;normalEstimation.setInputCloud(cloud_smoothed);pcl::search::KdTree<PointT>::Ptr tree(new pcl::search::KdTree<PointT>);normalEstimation.setSearchMethod(tree);pcl::PointCloud<pcl::Normal>::Ptr normals(new pcl::PointCloud<pcl::Normal>);normalEstimation.setKSearch(10);normalEstimation.compute(*normals);std::cout<<"normals: "<<normals->size()<<", "<<"normals fields: "<<pcl::getFieldsList(*normals)<<std::endl;pcl::io::savePCDFileASCII("normals.pcd",*normals);//Triangle Projectionpcl::PointCloud<pcl::PointNormal>::Ptr cloud_with_normals(new pcl::PointCloud<pcl::PointNormal>);pcl::concatenateFields(*cloud_smoothed,*normals,*cloud_with_normals);std::cout<<"cloud_with_normals fields: "<<pcl::getFieldsList(*cloud_with_normals)<<std::endl;pcl::io::savePCDFileASCII("cloud_with_normals.pcd",*cloud_with_normals);pcl::search::KdTree<pcl::PointNormal>::Ptr tree2(new pcl::search::KdTree<pcl::PointNormal>);tree2->setInputCloud(cloud_with_normals);pcl::GreedyProjectionTriangulation<pcl::PointNormal> gp3;pcl::PolygonMesh triangles;gp3.setSearchRadius(0.1);gp3.setMu(2.5);gp3.setMaximumNearestNeighbors(52);gp3.setMaximumAngle(2*M_PI/3);gp3.setMinimumAngle(M_PI/18);gp3.setMaximumSurfaceAngle(M_PI/4);gp3.setNormalConsistency(false);gp3.setInputCloud(cloud_with_normals);gp3.setSearchMethod(tree2);gp3.reconstruct(triangles);// ----------------------结束你的代码--------------------------//// 显示结果boost::shared_ptr<pcl::visualization::PCLVisualizer> viewer (new pcl::visualization::PCLVisualizer ("PCL Viewer"));viewer->setBackgroundColor (0, 0, 0);
/*pcl::visualization::PointCloudColorHandlerRGBField<PointT> rgb(cloud_smoothed);//colorviewer->addPointCloud<PointT> (cloud_smoothed, rgb, "smooth cloud");viewer->setPointCloudRenderingProperties (pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 3, "smooth cloud");viewer->addPointCloudNormals<PointT, pcl::Normal> (cloud_smoothed, normals, 10, 0.1, "normals");
*/viewer->addPolygonMesh(triangles,"GPTriangle");viewer->addText("GPTriangle",0,0,"GPTriangle");viewer->initCameraParameters ();while (!viewer->wasStopped ()){viewer->spinOnce (100);boost::this_thread::sleep (boost::posix_time::microseconds (100000));}return 0;
}参考：https://cloud.tencent.com/developer/article/1436518
可视化：
重建效果图2：
几个需要注意的地方：
1、贪婪三角化算法必须要求点云是平滑的，且密度变化均匀（这也是上一步为什么要做一个平滑处理）。
2、 贪婪投影三角化要求输入的点云必须是有向点云，所以对平滑后的点云进行法向量估计，并且将其Fields拼接在一起
3、拼接Fields的时候函数
pcl::concatenateFields只能接受PointXYZ类型的点云数据，对于平滑后的带有RGB信息的点云要将其以PointXYZ的类型读入。
4、重建结果显示是函数
viewer->addPolygonMesh(triangles,"GPTriangle");
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