Point Feature Histograms in PCL library output interpretation - c++

Question

I am using library called Point Cloud Library (PCL). In particular I am trying to compute point feature histograms. I followed this code from the website:
#include <pcl/point_types.h>
#include <pcl/features/pfh.h>
{
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud (new pcl::PointCloud<pcl::PointXYZ>);
pcl::PointCloud<pcl::Normal>::Ptr normals (new pcl::PointCloud<pcl::Normal> ());
... read, pass in or create a point cloud with normals ...
... (note: you can create a single PointCloud<PointNormal> if you want) ...
// Create the PFH estimation class, and pass the input dataset+normals to it
pcl::PFHEstimation<pcl::PointXYZ, pcl::Normal, pcl::PFHSignature125> pfh;
pfh.setInputCloud (cloud);
pfh.setInputNormals (normals);
// alternatively, if cloud is of tpe PointNormal, do pfh.setInputNormals (cloud);
// Create an empty kdtree representation, and pass it to the PFH 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> ());
//pcl::KdTreeFLANN<pcl::PointXYZ>::Ptr tree (new pcl::KdTreeFLANN<pcl::PointXYZ> ()); -- older call for PCL 1.5-
pfh.setSearchMethod (tree);
// Output datasets
pcl::PointCloud<pcl::PFHSignature125>::Ptr pfhs (new pcl::PointCloud<pcl::PFHSignature125> ());
// 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!!!
pfh.setRadiusSearch (0.05);
// Compute the features
pfh.compute (*pfhs);
// pfhs->points.size () should have the same size as the input cloud->points.size ()*
}
The output I get is an array of 125 values per point from the original point cloud. For example if I have a point cloud with 1000 points, where each point contains XYZ, then there will be 1000 * 125 values. I was able to understand why I have 125 entries where each corresponds to a bin. (assuming 3 features and 5 divisions 5^3 = 125)
This post helped some: PCL Point Feature Histograms - binning
Unfortunately I still have a few questions:
1) Why do I have a 125 histograms per point? Is it because it measures what is the percentage of points that are in K-nearest neighborhood to current point have similar features and then each point has its own neighborhood?
2) I see for some points all 125 entries are zeros. Why?
3) Graphing point feature histograms values as shown in the paper and website:
Website:
http://pointclouds.org/documentation/tutorials/pfh_estimation.php#pfh-estimation
Paper:
https://pdfs.semanticscholar.org/5aee/411f0b4228ba63c85df0e8ed64cab5844aed.pdf
The graphs shown have their X axis as number of bins (in my case 125 bins) so the natural question how do we consolidate 125 values per point into one graph?
I tried a simple summation of appropriate columns and scaling them by a constant but I do not think it is right. By summation I mean add all bin[0] for every point, next sum all bin[1] for every point and so on until bin[124].
I would really appreciate any help to clarify this.
Thank you.

Answer 1

The PFH descriptor is a local descriptor, so the histogram is computed for each point it is given. You likely will want to use only a keypoint or a set of kepypoints.
The histogram will have entries of 0 if it has no nearest neighbors within the radius search.
As for graphing, try viewing the histogram for one point at a time. I don't think it makes sense to consolidate it into one graph.
If you are interested in global descriptors that take into account all points, take a look at the CVFH descriptor (Clustered Viewpoint Feature Histogram) or other global ones.