论文标题
周围都是：3D对象检测的范围引导的圆柱网络
It's All Around You: Range-Guided Cylindrical Network for 3D Object Detection
论文作者
论文摘要
自主驾驶领域的现代感知系统依赖于3D数据分析。激光雷达传感器经常用于获取此类数据，因为它们对不同的照明条件的韧性提高。尽管旋转的激光雷达扫描仪在空间中产生环形图案，但大多数网络使用正交素抽样策略分析其数据。这项工作提出了一种新的方法，用于分析由360度深度扫描仪产生的3D数据，利用更合适的坐标系，该坐标系与扫描模式一致。此外，我们介绍了一个新颖的范围引导卷积的概念，通过距离自我车辆和物体的尺度来适应接受场。我们的网络在Nuscenes挑战中展示了与当前最新体系结构相当的结果。这项工作中引入的骨干结构也可以很容易地集成到其他管道上。
Modern perception systems in the field of autonomous driving rely on 3D data analysis. LiDAR sensors are frequently used to acquire such data due to their increased resilience to different lighting conditions. Although rotating LiDAR scanners produce ring-shaped patterns in space, most networks analyze their data using an orthogonal voxel sampling strategy. This work presents a novel approach for analyzing 3D data produced by 360-degree depth scanners, utilizing a more suitable coordinate system, which is aligned with the scanning pattern. Furthermore, we introduce a novel notion of range-guided convolutions, adapting the receptive field by distance from the ego vehicle and the object's scale. Our network demonstrates powerful results on the nuScenes challenge, comparable to current state-of-the-art architectures. The backbone architecture introduced in this work can be easily integrated onto other pipelines as well.
