  This article addresses the problem of distilling knowledge from a large
teacher model to a slim student network for LiDAR semantic segmentation.
Directly employing previous distillation approaches yields inferior results due
to the intrinsic challenges of point cloud, i.e., sparsity, randomness and
varying density. To tackle the aforementioned problems, we propose the
Point-to-Voxel Knowledge Distillation (PVD), which transfers the hidden
knowledge from both point level and voxel level. Specifically, we first
leverage both the pointwise and voxelwise output distillation to complement the
sparse supervision signals. Then, to better exploit the structural information,
we divide the whole point cloud into several supervoxels and design a
difficulty-aware sampling strategy to more frequently sample supervoxels
containing less-frequent classes and faraway objects. On these supervoxels, we
propose inter-point and inter-voxel affinity distillation, where the similarity
information between points and voxels can help the student model better capture
the structural information of the surrounding environment. We conduct extensive
experiments on two popular LiDAR segmentation benchmarks, i.e., nuScenes and
SemanticKITTI. On both benchmarks, our PVD consistently outperforms previous
distillation approaches by a large margin on three representative backbones,
i.e., Cylinder3D, SPVNAS and MinkowskiNet. Notably, on the challenging nuScenes
and SemanticKITTI datasets, our method can achieve roughly 75% MACs reduction
and 2x speedup on the competitive Cylinder3D model and rank 1st on the
SemanticKITTI leaderboard among all published algorithms. Our code is available
at https://github.com/cardwing/Codes-for-PVKD.
