DPA-RCNN: Dual Position Aware 3D Object Detector for Point Cloud

Yidong Jiang, Qing Xie, Jiachen Li, Jinyu Xu, Yongjian Liu, Yanchun Ma

Abstract: In this paper, we explore the impact of the spatial properties of point clouds on 3D object detection in autonomous driving scenarios. To reduce the memory and computational costs, existing point-based models typically use random sampling or the farthest point sampling strategy to retain the foreground points or points closer to the center of the object. However, they treated points with different spatial properties equally, which led to the loss of potential relative geometric information in the point cloud. To this end, we design a two-stage detector that fully exploits the spatial properties of point clouds, termed Dual Position Aware 3D Object Detector(DPA-RCNN). Specifically, we first sample more points close to the object center by exploiting the relative position information between points and the object center. These sampling points can generate more precisely positioned proposals, which can reduce the difficulty of subsequent bounding box regression stages. In addition, we use the distance from the point to the edge of the bounding box to learn edge features in proposals, which further improves the regression accuracy of the bounding box. Experiments on KITTI and ONCE datasets validate the superiority and universality of our approach. Our method outperforms all point-based detectors, and the proposed Edge Point-aware Segmentation Module(EPAS) can effectively improve the detection accuracy of two-stage detectors.