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：