LiDAR Distillation: Bridging the Beam-Induced Domain Gap for 3D Object Detection
This addresses a practical problem for robotics and autonomous vehicles using different LiDAR models, but it is incremental as it builds on existing teacher-student frameworks.
The paper tackles the domain gap in 3D object detection caused by varying LiDAR beam counts, proposing LiDAR Distillation to align point cloud densities and distill information from high-beam to low-beam data, achieving effectiveness across multiple datasets without added inference cost.
In this paper, we propose the LiDAR Distillation to bridge the domain gap induced by different LiDAR beams for 3D object detection. In many real-world applications, the LiDAR points used by mass-produced robots and vehicles usually have fewer beams than that in large-scale public datasets. Moreover, as the LiDARs are upgraded to other product models with different beam amount, it becomes challenging to utilize the labeled data captured by previous versions' high-resolution sensors. Despite the recent progress on domain adaptive 3D detection, most methods struggle to eliminate the beam-induced domain gap. We find that it is essential to align the point cloud density of the source domain with that of the target domain during the training process. Inspired by this discovery, we propose a progressive framework to mitigate the beam-induced domain shift. In each iteration, we first generate low-beam pseudo LiDAR by downsampling the high-beam point clouds. Then the teacher-student framework is employed to distill rich information from the data with more beams. Extensive experiments on Waymo, nuScenes and KITTI datasets with three different LiDAR-based detectors demonstrate the effectiveness of our LiDAR Distillation. Notably, our approach does not increase any additional computation cost for inference.