LiDAR sensor systems provide high resolution spatial information about the environment for self-driving cars. Therefore, detecting objects from point clouds derived from LiDAR represents a critical problem. Previous work on object detection from LiDAR has emphasized re-purposing convolutional approaches from traditional camera imagery. In this work, we present an object detection system designed specifically for point cloud data blending aspects of one-stage and two-stage systems. We observe that objects in point clouds are quite distinct from traditional camera images: objects are sparse and vary widely in location, but do not exhibit scale distortions observed in single camera perspective. These two observations suggest that simple and cheap data-driven object proposals to maximize spatial coverage or match the observed densities of point cloud data may suffice. This recognition paired with a local, non-convolutional, point-based network permits building an object detector for point clouds that may be trained only once, but adapted to different computational settings -- targeted to different predictive priorities or spatial regions. We demonstrate this flexibility and the targeted detection strategies on both the KITTI detection dataset as well as on the large-scale Waymo Open Dataset. Furthermore, we find that a single network is competitive with other point cloud detectors across a range of computational budgets, while being more flexible to adapt to contextual priorities.

本文提出了一个名为StarNet的基于点云的物体检测系统，利用了点云数据稀疏和三维特征的优点，采用采样策略和数据依赖的anchors，相比传统卷积方法能够更加灵活和高效地检测道路上行人和其他物体。同时，该系统还可以通过利用时间序列信息和利用先验信息和启发式算法精准地定位需要检测的区域。实验表明，StarNet相比于传统卷积基线具有更好的检测表现，并且可以在不重新训练的情况下根据需求灵活控制计算成本。

StarNet：针对点云目标检测的定向计算