前言：

       三维点云为三维欧式空间点的集合。对点云的形状描述若使用局部特征，则可分为两种：固定世界坐标系的局部描述和寻找局部主方向的局部描述，ROPS特征为寻找局部主方向的特征描述。

1.寻找主方向（对XYZ轴经过特定旋转）LFR：

         <1>.计算法线特征：这一步是非常耗计算量的，若达到可以接受的法线精度，此过程几乎占据了 整个计算过程的50%；可选择的方法有 使用空间树索引建立近邻域，对近邻平面拟合，平面的参数方向既是法线一个方向。

         <2>.进行多边形重建：利用贪婪投影的方法进行三角形重建，这个事一个调参数的过程，没有可以完全的方法。

                参数有：

         gp3.setSearchMethod (treeNor);
         gp3.setSearchRadius (Gp3PolyParam.SearchRadius);// Set 最大搜索半径
         gp3.setMu            (Gp3PolyParam.MuTypeValue);// Set typical values 
         gp3.setMaximumNearestNeighbors (Gp3PolyParam.MaximumNearestNeighbors);
         gp3.setMaximumSurfaceAngle  (Gp3PolyParam.MaximumSurfaceAngle); // 45 度
         gp3.setMinimumAngle               ( Gp3PolyParam.MinimumAngle); // 10 度
         gp3.setMaximumAngle                (Gp3PolyParam.MaximumAngle); // 120 度
         gp3.setNormalConsistency      (Gp3PolyParam.NormalConsistency);

         <3>.计算整幅图像的ROPS特征：

           查找PCL官网的tutoriales：http://pointclouds.org/documentation/tutorials/rops_feature.php。

#include <pcl/features/rops_estimation.h>
#include <pcl/io/pcd_io.h>

int main (int argc, char** argv)
{
  if (argc != 4)
    return (-1);

  pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ> ());
  if (pcl::io::loadPCDFile (argv[1], *cloud) == -1)
    return (-1);

  pcl::PointIndicesPtr indices = boost::shared_ptr <pcl::PointIndices> (new pcl::PointIndices ());
  std::ifstream indices_file;
  indices_file.open (argv[2], std::ifstream::in);
  for (std::string line; std::getline (indices_file, line);)
  {
    std::istringstream in (line);
    unsigned int index = 0;
    in >> index;
    indices->indices.push_back (index - 1);
  }
  indices_file.close ();

  std::vector <pcl::Vertices> triangles;
  std::ifstream triangles_file;
  triangles_file.open (argv[3], std::ifstream::in);
  for (std::string line; std::getline (triangles_file, line);)
  {
    pcl::Vertices triangle;
    std::istringstream in (line);
    unsigned int vertex = 0;
    in >> vertex;
    triangle.vertices.push_back (vertex - 1);
    in >> vertex;
    triangle.vertices.push_back (vertex - 1);
    in >> vertex;
    triangle.vertices.push_back (vertex - 1);
    triangles.push_back (triangle);
  }

  float support_radius = 0.0285f;
  unsigned int number_of_partition_bins = 5;
  unsigned int number_of_rotations = 3;

  pcl::search::KdTree<pcl::PointXYZ>::Ptr search_method (new pcl::search::KdTree<pcl::PointXYZ>);
  search_method->setInputCloud (cloud);

  pcl::ROPSEstimation <pcl::PointXYZ, pcl::Histogram <135> > feature_estimator;
  feature_estimator.setSearchMethod (search_method);
  feature_estimator.setSearchSurface (cloud);
  feature_estimator.setInputCloud (cloud);
  feature_estimator.setIndices (indices);
  feature_estimator.setTriangles (triangles);
  feature_estimator.setRadiusSearch (support_radius);
  feature_estimator.setNumberOfPartitionBins (number_of_partition_bins);
  feature_estimator.setNumberOfRotations (number_of_rotations);
  feature_estimator.setSupportRadius (support_radius);

  pcl::PointCloud<pcl::Histogram <135> >::Ptr histograms (new pcl::PointCloud <pcl::Histogram <135> > ());
  feature_estimator.compute (*histograms);

  return (0);
}