论文标题
鸟网+：LIDAR BIRD眼视图中的端到端3D对象检测
BirdNet+: End-to-End 3D Object Detection in LiDAR Bird's Eye View
论文作者
论文摘要
自动驾驶汽车中的板载3D对象检测通常依赖于激光雷达设备捕获的几何信息。尽管通常首选图像特征用于检测，但许多方法仅将空间数据作为输入。在推论中利用这些信息通常涉及使用紧凑的表示，例如鸟类的眼光（BEV）投影，这导致信息损失，从而阻碍了对象3D框的所有参数的联合推断。在本文中，我们提出了一个完全端到端的3D对象检测框架，该框架可以通过使用两个阶段的对象检测器和临时回归分支，仅从BEV图像中推断出定向的3D框，从而消除了对后处理阶段的需求。该方法的表现优于其前身（birdnet），并在评估中所有类别的Kitti 3D对象检测基准上获得了最先进的结果。
On-board 3D object detection in autonomous vehicles often relies on geometry information captured by LiDAR devices. Albeit image features are typically preferred for detection, numerous approaches take only spatial data as input. Exploiting this information in inference usually involves the use of compact representations such as the Bird's Eye View (BEV) projection, which entails a loss of information and thus hinders the joint inference of all the parameters of the objects' 3D boxes. In this paper, we present a fully end-to-end 3D object detection framework that can infer oriented 3D boxes solely from BEV images by using a two-stage object detector and ad-hoc regression branches, eliminating the need for a post-processing stage. The method outperforms its predecessor (BirdNet) by a large margin and obtains state-of-the-art results on the KITTI 3D Object Detection Benchmark for all the categories in evaluation.
