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.

该研究提出一种新的方法，利用适合扫描模式的坐标系来分析自动驾驶中 LiDAR 传感器所获取到的 3D 数据，并引入了基于范围的卷积方法，其能够根据自我车辆与物体的距离和物体的比例来调整感受野。实验结果在 nuscenes 竞赛上表现良好，可以与现有的先进架构相媲美，而该架构也可以轻松地整合到其他管道中。