PCL: 根据几何规则的曲面剖分-贪婪法表面重建三角网格

点云场景中进行物体识别，使用全局特征的方法严重依赖于点云分割，难以适应杂乱场景。使用局部特征，即对点云进行提取类似于3D SURF、ROPS之类的局部特征，需要寻找离散点云块的局部显著性。

点云的基本局部显著性有某一点处的曲率。

一、几何尺寸

可表述为显著性曲率的曲率阈值与物体的几何大小有关。

                

典型三维模型Dragon和ball两个物体，ball也可以进行三维剖分，但其三维剖分没有任何几何意义，而deagon的三维剖分有特异性。

二、无规则三角化

参考PCL官方网站链接：Fast triangulation of unordered point clouds

代码：

#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>

int

main (int argc, char** argv)

{

  // Load input file into a PointCloud<T> with an appropriate type

  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;

  pcl::PointCloud<pcl::Normal>::Ptr normals (new pcl::PointCloud<pcl::Normal>);

  pcl::search::KdTree<pcl::PointXYZ>::Ptr tree (new pcl::search::KdTree<pcl::PointXYZ>);

  tree->setInputCloud (cloud);

  n.setInputCloud (cloud);

  n.setSearchMethod (tree);

  n.setKSearch (20);

  n.compute (*normals);

  //* 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);

  //* cloud_with_normals = cloud + normals

  // Create search tree*

  pcl::search::KdTree<pcl::PointNormal>::Ptr tree2 (new pcl::search::KdTree<pcl::PointNormal>);

  tree2->setInputCloud (cloud_with_normals);

  // Initialize objects

  pcl::GreedyProjectionTriangulation<pcl::PointNormal> gp3;

  pcl::PolygonMesh triangles;

  // Set the maximum distance between connected points (maximum edge length)

  gp3.setSearchRadius (0.025);

  // Set typical values for the parameters

  gp3.setMu (2.5);

  gp3.setMaximumNearestNeighbors (100);

  gp3.setMaximumSurfaceAngle(M_PI/4); // 45 degrees

  gp3.setMinimumAngle(M_PI/18); // 10 degrees

  gp3.setMaximumAngle(2*M_PI/3); // 120 degrees

  gp3.setNormalConsistency(false);

  // Get result

  gp3.setInputCloud (cloud_with_normals);

  gp3.setSearchMethod (tree2);

  gp3.reconstruct (triangles);

  // Additional vertex information

  std::vector<int> parts = gp3.getPartIDs();

  std::vector<int> states = gp3.getPointStates();

  // Finish

  return (0);

}

图形效果：