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 analytic 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 plug-and-play for
various 3D deep architectures and also performs well for unsupervised domain
adaptation.

PolarMix 是一种点云数据增强技术，通过两个跨扫描的增强策略实现数据的丰富和保真，能够在不同的感知任务和场景中有效缓解数据限制，并能为各种 3D 深度架构提供即插即用，并且在无监督领域适应中也表现出良好的性能。