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.
翻译:LiDAR 点云通常通过连续旋转 LiDAR 传感器进行扫描,对周围环境进行精确的几何测量,对许多自主探测和导航任务至关重要。虽然已经开发了许多三维深层结构,但高效收集和批注大量点云仍然是分析和理解点云数据过程中的一大挑战。本文展示了极地云层增强技术,这是点云增强技术,简单和通用,但可以在不同的感知任务和情景中有效减轻数据限制。极地混合通过两个跨扫描增强战略丰富点云层分布,并保存点云的准确性,在扫描方向上截断、编辑和混合点云层。第一个是场景级交换点云层部分,在对点云层轴中交换两个LiDAR扫描的点云层部分。第二个是实例级旋转和粘度,作物从一个LIDAR 扫描中点点点点点点点点点点,用多个角度进行旋转(以创建多个副本),并将旋转点图案粘贴到其他扫描中。广泛的实验显示, 极地Mix 能够在不同的视野和深度模型中持续地段上运行。