BEV-Net: A Bird’s Eye View Object Detection Network for LiDAR Point Cloud
PubDate: December 2021
Teams: Beihang University
Writers: Meng Liu; Jianwei Niu
PDF: BEV-Net: A Bird’s Eye View Object Detection Network for LiDAR Point Cloud
Abstract
LiDAR-only object detection is essential for autonomous driving systems and is a challenging problem. For the representation of a bird’s eye view LiDAR point-cloud, this paper proposes a single-stage object detector. The detector can output classification information and accurate positioning information for multi-category objects. In this paper, the detector’s design methods are detailed from a bird’s eye view LiDAR point-cloud encoding, network design, data augmentation, etc. The detector was evaluated on three challenging datasets: KITTI, nuScenes and Waymo. The experimental results demonstrated that the proposed detector can accurately achieve object detection tasks and the detection speed can reach 26.9 FPS. Both the precision and the speed can meet the requirements of most autonomous driving scenarios.