论文标题
基于LIDAR的3D对象检测的多功能多视图框架，并通过全景分段指导
A Versatile Multi-View Framework for LiDAR-based 3D Object Detection with Guidance from Panoptic Segmentation
论文作者
论文摘要
使用LIDAR数据的3D对象检测是自动驾驶系统必不可少的组件。但是，只有少数基于激光雷达的3D对象检测方法利用分割信息来进一步指导检测过程。在本文中，我们提出了一个新型的多任务框架，该框架共同执行3D对象检测和全盘分段。在我们的方法中，通过注入3D泛型分段骨架的射程视图（RV）特征图来增强鸟眼视图（BEV）平面中的3D对象检测主链。这使检测主链能够利用多视图信息来解决每个投影视图的缺点。此外，通过突出特征地图中每个对象类的位置来重点介绍前景语义信息以简化检测任务。最后，基于实例级信息生成的新中心密度热图通过建议可能的对象中心位置，从而进一步指导检测主链。我们的方法可与任何基于BEV的3D对象检测方法一起使用，如Nuscenes数据集上的广泛实验所示，它提供了显着的性能增长。值得注意的是，基于单阶段中心点3D对象检测网络的提议方法在Nuscenes 3D检测基准上实现了最新的性能，具有67.3 nds。
3D object detection using LiDAR data is an indispensable component for autonomous driving systems. Yet, only a few LiDAR-based 3D object detection methods leverage segmentation information to further guide the detection process. In this paper, we propose a novel multi-task framework that jointly performs 3D object detection and panoptic segmentation. In our method, the 3D object detection backbone in Bird's-Eye-View (BEV) plane is augmented by the injection of Range-View (RV) feature maps from the 3D panoptic segmentation backbone. This enables the detection backbone to leverage multi-view information to address the shortcomings of each projection view. Furthermore, foreground semantic information is incorporated to ease the detection task by highlighting the locations of each object class in the feature maps. Finally, a new center density heatmap generated based on the instance-level information further guides the detection backbone by suggesting possible box center locations for objects. Our method works with any BEV-based 3D object detection method, and as shown by extensive experiments on the nuScenes dataset, it provides significant performance gains. Notably, the proposed method based on a single-stage CenterPoint 3D object detection network achieved state-of-the-art performance on nuScenes 3D Detection Benchmark with 67.3 NDS.
