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{https://github.com/skyshoumeng/M2SKD}.

翻译：三维点云语义分割是环境理解的基本任务之一。尽管近年取得了显著进展，但少样本或少点类别的性能仍远未令人满意。本文针对三维点云语义分割任务，提出了一种新颖的多对单知识蒸馏框架，以提升这些困难类别的性能。我们并非直接融合多扫描的所有点，而是仅融合属于先前定义的困难类别的实例。为了有效且充分地蒸馏多扫描中的有价值知识，我们采用了多层级蒸馏框架，即特征表示蒸馏、逻辑蒸馏和亲和力蒸馏。我们进一步开发了一种新颖的实例感知亲和力蒸馏算法，用于捕获高层结构知识，以增强困难类别的蒸馏效果。最后，我们在SemanticKITTI数据集上进行了实验，验证集和测试集的结果均表明，与基线方法相比，我们的方法取得了显著提升。代码已在\Url{https://github.com/skyshoumeng/M2SKD}上开源。