Simulated LiDAR Repositioning: a novel point cloud data augmentation method

TL;DR

This paper introduces a new data augmentation technique for LiDAR scans by simulating scans from different positions, improving the diversity and utility of point cloud data for various applications.

Contribution

The method defines criteria for selecting secondary positions and estimates point clouds from these positions, enhancing LiDAR data augmentation with validated accuracy in synthetic scenes.

Findings

More accurate at short distances

High scanner resolution improves similarity

Effective for natural scene statistics

Abstract

We address a data augmentation problem for LiDAR. Given a LiDAR scan of a scene from some position, how can one simulate new scans of that scene from different, secondary positions? The method defines criteria for selecting valid secondary positions, and then estimates which points from the original point cloud would be acquired by a scanner from these positions. We validate the method using synthetic scenes, and examine how the similarity of generated point clouds depends on scanner distance, occlusion, and angular resolution. We show that the method is more accurate at short distances, and that having a high scanner resolution for the original point clouds has a strong impact on the similarity of generated point clouds. We also demonstrate how the method can be applied to natural scene statistics: in particular, we apply our method to reposition the scanner horizontally and vertically, separately consider points belonging to the ground and to non-ground objects, and describe the impact on the distributions of distances to these two classes of points.