Document Type

Conference Proceeding

Publication Title

Advances in Neural Information Processing Systems

Abstract

LiDAR point clouds, which are usually scanned by rotating LiDAR sensors continuously, capture precise geometry of the surrounding environment and are crucial to many autonomous detection and navigation tasks. Though many 3D deep architectures have been developed, efficient collection and annotation of large amounts of point clouds remain one major challenge in the analytics and understanding of point cloud data. This paper presents PolarMix, a point cloud augmentation technique that is simple and generic but can mitigate the data constraint effectively across different perception tasks and scenarios. PolarMix enriches point cloud distributions and preserves point cloud fidelity via two cross-scan augmentation strategies that cut, edit, and mix point clouds along the scanning direction. The first is scene-level swapping which exchanges point cloud sectors of two LiDAR scans that are cut along the azimuth axis. The second is instance-level rotation and paste which crops point instances from one LiDAR scan, rotates them by multiple angles (to create multiple copies), and paste the rotated point instances into other scans. Extensive experiments show that PolarMix achieves superior performance consistently across different perception tasks and scenarios. In addition, it can work as a plug-and-play for various 3D deep architectures and also performs well for unsupervised domain adaptation. Code is available at https://github.com/xiaoaoran/polarmix.

Publication Date

12-9-2022

Keywords

Optical radar, Augmentation techniques, Autonomous detection, Autonomous navigation, Data augmentation, Deep architectures, Detection tasks, Large amounts, Navigation tasks, Point-clouds, Surrounding environment

Comments

Open access version by NeurIPS

Uploaded 28 May 2024

Download

Share

COinS