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LiDAR point clouds, which are usually scanned by rotating LiDAR sensorscontinuously, capture precise geometry of the surrounding environment and arecrucial to many autonomous detection and navigation tasks. Though many 3D deeparchitectures have been developed, efficient collection and annotation of largeamounts of point clouds remain one major challenge in the analytic andunderstanding of point cloud data. This paper presents PolarMix, a point cloudaugmentation technique that is simple and generic but can mitigate the dataconstraint effectively across different perception tasks and scenarios.PolarMix enriches point cloud distributions and preserves point cloud fidelityvia two cross-scan augmentation strategies that cut, edit, and mix point cloudsalong the scanning direction. The first is scene-level swapping which exchangespoint cloud sectors of two LiDAR scans that are cut along the azimuth axis. Thesecond is instance-level rotation and paste which crops point instances fromone LiDAR scan, rotates them by multiple angles (to create multiple copies),and paste the rotated point instances into other scans. Extensive experimentsshow that PolarMix achieves superior performance consistently across differentperception tasks and scenarios. In addition, it can work as plug-and-play forvarious 3D deep architectures and also performs well for unsupervised domainadaptation.

PolarMix: A General Data Augmentation Technique for LiDAR Point Clouds