use EuclideanClusterExtraction for FPFH descriptors

[XuankeShi]

i've try enclideadClusterExtraction for fpfh for each point, but it did't work,is there anyone who has tried it ,i need your help. thx very much.And i wanna get the result as follows:
Surfaces with the same geometric features come together。

[XuankeShi]

i've try enclideadClusterExtraction for fpfh for each point, but it did't work,is there anyone who has tried it ,i need your help. thx very much.

The code is as follows:

#include
#include <pcl/io/pcd_io.h>
#include <pcl/visualization/pcl_visualizer.h>
#include <pcl/point_types.h>
#include <pcl/features/normal_3d_omp.h>
#include <pcl/segmentation/extract_clusters.h>
#include <pcl/kdtree/kdtree.h>
#include <pcl/filters/extract_indices.h>
#include <pcl/filters/voxel_grid.h>
#include <pcl/features/fpfh.h>
#include <pcl/features/pfh.h>
#include <pcl/segmentation/impl/extract_clusters.hpp>
#include <pcl/search/impl/search.hpp>

double
computeCloudResolution(const pcl::PointCloudpcl::PointXYZ::ConstPtr& cloud)
{
double resolution = 0.0;
int numberOfPoints = 0;
int nres;
std::vector indices(2);
std::vector squaredDistances(2);
pcl::search::KdTreepcl::PointXYZ tree;
tree.setInputCloud(cloud);

for (size_t i = 0; i < cloud->size(); ++i)
{
	if (!pcl_isfinite((*cloud)[i].x))
		continue;

	// Considering the second neighbor since the first is the point itself.
	nres = tree.nearestKSearch(i, 2, indices, squaredDistances);
	if (nres == 2)
	{
		resolution += sqrt(squaredDistances[1]);
		++numberOfPoints;
	}
}
if (numberOfPoints != 0)
	resolution /= numberOfPoints;

return resolution;

}

int main(int argc, char **argv)
{
pcl::PointCloudpcl::PointXYZ::Ptr scene(new pcl::PointCloudpcl::PointXYZ());
pcl::PointCloudpcl::Normal::Ptr normals(new pcl::PointCloudpcl::Normal());
pcl::PointCloudpcl::FPFHSignature33::Ptr fpfhs(new pcl::PointCloudpcl::FPFHSignature33());
if (pcl::io::loadPCDFile("milk_cartoon_all_small_clorox.pcd",*scene) == -1)
{
PCL_ERROR("can't open the pcd file ");
system("pause ");
return -1;
}

double resolution = 2*computeCloudResolution(scene);
pcl::VoxelGrid<pcl::PointXYZ> vg;
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_filtered(new pcl::PointCloud<pcl::PointXYZ>);
vg.setInputCloud(scene);
vg.setLeafSize(resolution, resolution, resolution);
vg.filter(*cloud_filtered);
std::cout << "PointCloud after filtering has: " << cloud_filtered->points.size() << " data points." << std::endl; 
pcl::search::KdTree<pcl::PointXYZ>::Ptr kdtree(new pcl::search::KdTree<pcl::PointXYZ>());
kdtree->setInputCloud(cloud_filtered);
pcl::NormalEstimationOMP<pcl::PointXYZ, pcl::Normal> ne;
ne.setNumberOfThreads(3);
ne.setInputCloud(cloud_filtered);
ne.setSearchMethod(kdtree);
ne.setRadiusSearch(5 * resolution);
ne.setViewPoint(0,0,0);
ne.compute(*normals);


pcl::FPFHEstimation<pcl::PointXYZ, pcl::Normal, pcl::FPFHSignature33> fpfh;
fpfh.setInputCloud(cloud_filtered);
fpfh.setInputNormals(normals);
pcl::search::KdTree<pcl::PointXYZ>::Ptr tree(new pcl::search::KdTree<pcl::PointXYZ>());
fpfh.setSearchMethod(tree);
fpfh.compute(*fpfhs);

//std::vector<pcl::PointIndices> cluster_indices;
std::vector<pcl::PointIndices> cluster_indices;
pcl::EuclideanClusterExtraction<pcl::FPFHSignature33> ec;
ec.setClusterTolerance(10000);
ec.setMinClusterSize(50);
ec.setMaxClusterSize(200000);
pcl::search::KdTree<pcl::FPFHSignature33>::Ptr kdtree2(new pcl::search::KdTree<pcl::FPFHSignature33>);
kdtree2->setInputCloud(fpfhs);
ec.setSearchMethod(kdtree2);
ec.setInputCloud(fpfhs);
ec.extract(cluster_indices);

}

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