PCL点云曲面重建（1）

#include <pcl/point_types.h>
#include <pcl/io/pcd_io.h>
#include <pcl/kdtree/kdtree_flann.h>  //kd-tree搜索对象的类定义的头文件
#include <pcl/surface/mls.h>        //最小二乘法平滑处理类定义头文件

intmain (int argc, char** argv)
{
 pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ> ());
 pcl::io::loadPCDFile ("bun0.pcd", *cloud);  // 创建 KD-Tree
 pcl::search::KdTree<pcl::PointXYZ>::Ptr tree (new pcl::search::KdTree<pcl::PointXYZ>);  // Output has the PointNormal type in order to store the normals calculated by MLS
 pcl::PointCloud<pcl::PointNormal> mls_points;  // 定义最小二乘实现的对象mls
 pcl::MovingLeastSquares<pcl::PointXYZ, pcl::PointNormal> mls;
 mls.setComputeNormals (true);  //设置在最小二乘计算中需要进行法线估计  
// Set parameters  
  mls.setInputCloud (cloud);
 mls.setPolynomialFit (true);
 mls.setSearchMethod (tree);
 mls.setSearchRadius (0.03);  

// Reconstruct  
  mls.process (mls_points);  // Save output
 pcl::io::savePCDFile ("bun0-mls.pcd", mls_points);
}

#include <pcl/ModelCoefficients.h>           //采样一致性模型相关类头文件
#include <pcl/io/pcd_io.h>
#include <pcl/point_types.h>
#include <pcl/sample_consensus/method_types.h>
#include <pcl/sample_consensus/model_types.h>
#include <pcl/filters/passthrough.h>
#include <pcl/filters/project_inliers.h>          //滤波相关类头文件
#include <pcl/segmentation/sac_segmentation.h>   //基于采样一致性分割类定义的头文件#include <pcl/surface/concave_hull.h>                 //创建凹多边形类定义头文件intmain (int argc, char** argv)
{
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>);
pcl::PCDReader reader;
reader.read ("table_scene_mug_stereo_textured.pcd", *cloud);  // 建立过滤器消除杂散的NaN
 pcl::PassThrough<pcl::PointXYZ> pass;
 pass.setInputCloud (cloud);                  //设置输入点云
 pass.setFilterFieldName ("z");             //设置分割字段为z坐标
 pass.setFilterLimits (0, 1.1);             //设置分割阀值为(0, 1.1)
 pass.filter (*cloud_filtered);              
 std::cerr << "PointCloud after filtering has: "
           << cloud_filtered->points.size () << " data points." << std::endl; pcl::ModelCoefficients::Ptr coefficients (new pcl::ModelCoefficients);
 pcl::PointIndices::Ptr inliers (new pcl::PointIndices);   //inliers存储分割后的点云  
// 创建分割对象
 pcl::SACSegmentation<pcl::PointXYZ> seg;  // 设置优化系数，该参数为可选参数
 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;  // Project the model inliers
 pcl::ProjectInliers<pcl::PointXYZ> proj;//点云投影滤波模型
 proj.setModelType (pcl::SACMODEL_PLANE); //设置投影模型  
  proj.setIndices (inliers);            
 proj.setInputCloud (cloud_filtered);
 proj.setModelCoefficients (coefficients); //将估计得到的平面coefficients参数设置为投影平面模型系数
 proj.filter (*cloud_projected); //得到投影后的点云
 std::cerr << "PointCloud after projection has: "
           << cloud_projected->points.size () << " data points." << std::endl;  // 存储提取多边形上的点云
 pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_hull (new pcl::PointCloud<pcl::PointXYZ>);
 pcl::ConcaveHull<pcl::PointXYZ> chull;        //创建多边形提取对象
 chull.setInputCloud (cloud_projected);       //设置输入点云为提取后点云
 chull.setAlpha (0.1);
 chull.reconstruct (*cloud_hull);   //创建提取创建凹多边形
 std::cerr << "Concave hull has: " << cloud_hull->points.size () << " data points." << std::endl; pcl::PCDWriter writer;
 writer.write ("table_scene_mug_stereo_textured_hull.pcd", *cloud_hull, false);  return (0);
}

#include <pcl/point_types.h>
#include <pcl/io/pcd_io.h>
#include <pcl/kdtree/kdtree_flann.h>
#include <pcl/features/normal_3d.h>
#include <pcl/surface/gp3.h>      //贪婪投影三角化算法

int main (int argc, char** argv)
{  // 将一个XYZ点类型的PCD文件打开并存储到对象中

pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ>);
 pcl::PCLPointCloud2 cloud_blob;
 pcl::io::loadPCDFile ("bun0.pcd", cloud_blob);
 pcl::fromPCLPointCloud2 (cloud_blob, *cloud);  //* the data should be available in cloud  
// Normal estimation*
 pcl::NormalEstimation<pcl::PointXYZ, pcl::Normal> n;      //法线估计对象
 pcl::PointCloud<pcl::Normal>::Ptr normals (new pcl::PointCloud<pcl::Normal>);   //存储估计的法线
 pcl::search::KdTree<pcl::PointXYZ>::Ptr tree (new pcl::search::KdTree<pcl::PointXYZ>);  //定义kd树指针
 tree->setInputCloud (cloud);   ///用cloud构建tree对象
 n.setInputCloud (cloud);
 n.setSearchMethod (tree);
 n.setKSearch (20);
 n.compute (*normals);       ////估计法线存储到其中
 //* normals should not contain the point normals + surface curvatures  // Concatenate the XYZ and normal fields*
 pcl::PointCloud<pcl::PointNormal>::Ptr cloud_with_normals (new pcl::PointCloud<pcl::PointNormal>);
 pcl::concatenateFields (*cloud, *normals, *cloud_with_normals);    //连接字段  //* cloud_with_normals = cloud + normals  //定义搜索树对象
 pcl::search::KdTree<pcl::PointNormal>::Ptr tree2 (new pcl::search::KdTree<pcl::PointNormal>);
 tree2->setInputCloud (cloud_with_normals);   //点云构建搜索树  

// Initialize objects
 pcl::GreedyProjectionTriangulation<pcl::PointNormal> gp3;   //定义三角化对象
 pcl::PolygonMesh triangles;                //存储最终三角化的网络模型
 // Set the maximum distance between connected points (maximum edge length)
 gp3.setSearchRadius (0.025);  //设置连接点之间的最大距离，（即是三角形最大边长）  // 设置各参数值
 gp3.setMu (2.5);  //设置被样本点搜索其近邻点的最远距离为2.5，为了使用点云密度的变化
 gp3.setMaximumNearestNeighbors (100);    //设置样本点可搜索的邻域个数
 gp3.setMaximumSurfaceAngle(M_PI/4); // 设置某点法线方向偏离样本点法线的最大角度45
 gp3.setMinimumAngle(M_PI/18); // 设置三角化后得到的三角形内角的最小的角度为10
 gp3.setMaximumAngle(2*M_PI/3); // 设置三角化后得到的三角形内角的最大角度为120
 gp3.setNormalConsistency(false);  //设置该参数保证法线朝向一致  // Get result
 gp3.setInputCloud (cloud_with_normals);     //设置输入点云为有向点云
 gp3.setSearchMethod (tree2);   //设置搜索方式
 gp3.reconstruct (triangles);  //重建提取三角化  // 附加顶点信息
 std::vector<int> parts = gp3.getPartIDs();
 std::vector<int> states = gp3.getPointStates();  // Finish
 return (0);
}