  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.
