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::PointCloud::ConstPtr& cloud) { double resolution = 0.0; int numberOfPoints = 0; int nres; std::vector indices(2); std::vector squaredDistances(2); pcl::search::KdTree 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::PointCloud::Ptr scene(new pcl::PointCloud()); pcl::PointCloud::Ptr normals(new pcl::PointCloud()); pcl::PointCloud::Ptr fpfhs(new pcl::PointCloud()); 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);

}

[stale[bot]]

Marking this as stale due to 30 days of inactivity. It will be closed in 7 days if no further activity occurs.