Fast Point Feature Histograms (FPFH)

执行效率慢，占用大量CPU，最终计算PFH的点云大小和输入的点云大小相同，即fpfhs->points.size() s= cloud->points.size()

http://www.pointclouds.org/documentation/tutorials/fpfh_estimation.php#fpfh-estimation

#include <pcl/features/fpfh_omp.h>

pcl::PointCloud<pcl::PointXYZ>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZ>);


// Create the FPFH estimation class, and pass the input dataset+normals to it

pcl::FPFHEstimation<pcl::PointXYZ, pcl::Normal, pcl::FPFHSignature33> fpfh;
//使用OMP多线程加速执行，待验证
//pcl::FPFHEstimationOMP<pcl::PointXYZ, pcl::Normal, pcl::FPFHSignature33> fpfh;
//fpfh.setNumberOfThreads(8);

fpfh.setInputCloud(cloud);
fpfh.setInputNormals(normals);
// alternatively, if cloud is of tpe PointNormal, do fpfh.setInputNormals (cloud);

// Create an empty kdtree representation, and pass it to the FPFH estimation object.
// Its content will be filled inside the object, based on the given input dataset (as no other search surface is given).
pcl::search::KdTree<pcl::PointXYZ>::Ptr tree(new pcl::search::KdTree<pcl::PointXYZ>);

fpfh.setSearchMethod(tree);

// Output datasets
pcl::PointCloud<pcl::FPFHSignature33>::Ptr fpfhs(new pcl::PointCloud<pcl::FPFHSignature33>());

// Use all neighbors in a sphere of radius 5cm
// IMPORTANT: the radius used here has to be larger than the radius used to estimate the surface normals!!!
fpfh.setRadiusSearch(0.05);

// Compute the features
fpfh.compute(*fpfhs);

在计算FPFH时，考虑到效率的问题，没有对法向量进行空和无穷大检测，因此在计算FPH前需要进行法向量的判断，使用如下代码：

for (int i = 0; i < normals->points.size(); i++)
{
  if (!pcl::isFinite<pcl::Normal>(normals->points[i]))
  {
    PCL_WARN("normals[%d] is not finite\n", i);
  }
}

来源：oschina

链接：https://my.oschina.net/u/4228078/blog/3134885

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pcl

PCL1.8.1 Feature

Fast Point Feature Histograms (FPFH)