This paper studies point cloud perception within outdoor environments. Existing methods face limitations in recognizing objects located at a distance or occluded, due to the sparse nature of outdoor point clouds. In this work, we observe a significant mitigation of this problem by accumulating multiple temporally consecutive point cloud sweeps, resulting in a remarkable improvement in perception accuracy. However, the computation cost also increases, hindering previous approaches from utilizing a large number of point cloud sweeps. To tackle this challenge, we find that a considerable portion of points in the accumulated point cloud is redundant, and discarding these points has minimal impact on perception accuracy. We introduce a simple yet effective Gumbel Spatial Pruning (GSP) layer that dynamically prunes points based on a learned end-to-end sampling. The GSP layer is decoupled from other network components and thus can be seamlessly integrated into existing point cloud network architectures. Without incurring additional computational overhead, we increase the number of point cloud sweeps from 10, a common practice, to as many as 40. Consequently, there is a significant enhancement in perception performance. For instance, in nuScenes 3D object detection and BEV map segmentation tasks, our pruning strategy improves several 3D perception baseline methods.

翻译：本文研究室外环境下的点云感知问题。现有方法因室外点云的稀疏性，在识别远处或被遮挡物体时存在局限性。通过累积多个时间连续的点云扫描，我们观察到这一问题得到了显著缓解，感知精度获得明显提升。然而，计算成本也随之增加，导致先前方法无法利用大量点云扫描。针对这一挑战，我们发现累积点云中存在大量冗余点，剔除这些点对感知精度影响甚微。为此，我们引入一种简单而有效的Gumbel空间裁剪层，该层基于端到端学习进行动态点云裁剪。GSP层与网络其他组件解耦，可无缝集成至现有点云网络架构中。在不增加额外计算开销的情况下，我们将点云扫描次数从常规的10次提升至40次。最终，感知性能得到显著提升。例如，在nuScenes 3D目标检测与BEV地图分割任务中，我们的裁剪策略改进了多个3D感知基线方法。