Performance evaluation of 3D keypoints and descriptors

Abstract

This paper presents a comprehensive evaluation of the performance of common 3D keypoint detectors and descriptors currently available in the Point Cloud Library (PCL) to recover the transformation of 300 real objects. Current research on keypoints detectors and descriptors considers their performance individually in terms of their repeatability or descriptiveness, rather than on their overall performance at multi-sensor alignment or recovery. We present the data on the performance of each pair under all transformations independently: translations and rotations in and around each of the x-, y- and z-axis respectively. We provide insight into the implementation of the detectors and descriptors in PCL leading to abnormal or unexpected performance. The obtained results show that the ISS/SHOT and ISS/SHOTColor detector/descriptor pair works best at 3D recovery under various transformations.