Noise Robustness Analysis of Point Cloud Descriptors

Abstract

In this paper, we investigate the effect of noise on 3D point cloud descriptors. Various types of point cloud descriptors have been introduced in the recent years due to advances in computing power, which makes processing point cloud data more feasible. Most of these descriptors describe the orientation difference between pairs of 3D points in the object and represent these differences in a histogram. Earlier studies dealt with the performances of different point cloud descriptors; however, no study has ever discussed the effect of noise on the descriptors performances. This paper presents a comparison of performance for nine different local and global descriptors amidst 10 varying levels of Gaussian and impulse noises added to the point cloud data. The study showed that 3D descriptors are more sensitive to Gaussian noise compared to impulse noise. Surface normal based descriptors are sensitive to Gaussian noise but robust to impulse noise. While descriptors which are based on point’s accumulation in a spherical grid are more robust to Gaussian noise but sensitive to impulse noise. Among global descriptors, view point features histogram (VFH) descriptor gives good compromise between accuracy, stability and computational complexity against both Gaussian and impulse noises. SHOT (signature of histogram of orientations) descriptor is the best among the local descriptors and it has good performance for both Gaussian and impulse noises.