3D point cloud semantic segmentation is one of the fundamental tasks for
environmental understanding. Although significant progress has been made in
recent years, the performance of classes with few examples or few points is
still far from satisfactory. In this paper, we propose a novel multi-to-single
knowledge distillation framework for the 3D point cloud semantic segmentation
task to boost the performance of those hard classes. Instead of fusing all the
points of multi-scans directly, only the instances that belong to the
previously defined hard classes are fused. To effectively and sufficiently
distill valuable knowledge from multi-scans, we leverage a multilevel
distillation framework, i.e., feature representation distillation, logit
distillation, and affinity distillation. We further develop a novel
instance-aware affinity distillation algorithm for capturing high-level
structural knowledge to enhance the distillation efficacy for hard classes.
Finally, we conduct experiments on the SemanticKITTI dataset, and the results
on both the validation and test sets demonstrate that our method yields
substantial improvements compared with the baseline method. The code is
available at \Url{this https URL}.

本文提出了一种基于多源知识蒸馏的 3D 点云语义分割方法，采用多到单的融合方式，通过实例感知亲和性蒸馏算法获取高级结构知识，实现对于难分类类别的提升。在 SemanticKITTI 数据集中实验证明，该方法在验证集和测试集上均显著优于基线方法。