提出一种快速注意力点云简化方法，兼顾精度与实时性。
Fast Attention-Based Simplification of LiDAR Point Clouds for Object Detection and Classification
- 用注意力机制筛选任务相关区域，动态选择关键点。
- 在KITTI上比FPS快，采样率高时精度仍保持甚至更优。
- 适合自动驾驶中需高速处理点云的场景。
LiDAR点云广泛用于自动驾驶，以高频采集大量3D点表示车辆、行人和交通标志等物体。虽然密集数据提升感知精度，但也带来计算成本和功耗上升，限制实时部署。现有采样方法普遍存在权衡：极快方法牺牲精度，高精度方法计算开销大。为此，本文提出一种高效学习型点云简化方法，结合特征嵌入模块与基于注意力的采样模块，端到端训练，优先保留任务相关区域。在KITTI数据集上的3D目标检测及四个数据集上的分类任务中评估，该方法显著优于远点采样（FPS），速度更快且在极端降采样下精度更优；虽慢于随机采样（RS），但在高采样率下能更稳定保持精度。
原文摘要 · Abstract (English)
LiDAR point clouds are widely used in autonomous driving and consist of large numbers of 3D points captured at high frequency to represent surrounding objects such as vehicles, pedestrians, and traffic signs. While this dense data enables accurate perception, it also increases computational cost and power consumption, which can limit real-time deployment. Existing point cloud sampling methods typically face a trade-off: very fast approaches tend to reduce accuracy, while more accurate methods are computationally expensive. To address this limitation, we propose an efficient learned point cloud simplification method for LiDAR data. The method combines a feature embedding module with an attention-based sampling module to prioritize task-relevant regions and is trained end-to-end. We evaluate the method against farthest point sampling (FPS) and random sampling (RS) on 3D object detection on the KITTI dataset and on object classification across four datasets. The method was consistently faster than FPS and achieved similar, and in some settings better, accuracy, with the largest gains under aggressive downsampling. It was slower than RS, but it typically preserved accuracy more reliably at high sampling ratios.
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