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Hi adrom,

I'm currently writing my master thesis about feature based person detection in 3D data, so maybe I can help you getting an overview of the available features.

First important thing to know is, do you wanne do object detection or object recognition? As for the latter you (normally) match one point cloud to another to compare the similarity between both, features like FPFH or SURE (Surface Entropy by Fiolka, 2012) might be a good choice, as you can compare the similarity of the source object to the target object by computing the distance between the feature descriptors. Other Features for this task might be BRIEF, ORB, BRISK, FREAK and NARF.

For object detection the pure FPFH might not be such a good choice, as you are getting one feature histogram for each point of your point cloud. But you may combine the FPFH with some clustering algorithm or take kind of an average of the histogram to get one descriptor for your object (or a part of your object).

Beside this, there are a couple features adapting the HOG-idear on depth data: HOD (Histogram of Oriented Depth) HDD (Histogram of Depth Difference) RDSF (Relational Depth Similarities)

Some others are using the orientation of the normal vector, as FPFH is doing: HLSN (Histogram of Local Surface Normal -> working on pointclouds) HONV (Histogram of Oriented Normal Vectors -> working on depth map)

There is code for the SURE-Features online and the FPFH is of course part of the pcl. I couldn't find any code for the other features online, but especially the 4 latter shouldn't be too hard to implement.