LiDAR panoptic segmentation is a newly proposed technical task for autonomous driving. In contrast to popular end-to-end deep learning solutions, we propose a hybrid method with an existing semantic segmentation network to extract semantic information and a traditional LiDAR point cloud cluster algorithm to split each instance object. We argue geometry-based traditional clustering algorithms are worth being considered by showing a state-of-the-art performance among all published end-to-end deep learning solutions on the panoptic segmentation leaderboard of the SemanticKITTI dataset. To our best knowledge, we are the first to attempt the point cloud panoptic segmentation with clustering algorithms. Therefore, instead of working on new models, we give a comprehensive technical survey in this paper by implementing four typical cluster methods and report their performances on the benchmark. Those four cluster methods are the most representative ones with real-time running speed. They are implemented with C++ in this paper and then wrapped as a python function for seamless integration with the existing deep learning frameworks. We release our code for peer researchers who might be interested in this problem.

本文提出了一种混合方法，结合现有语义分割网络和传统的LiDAR点云聚类算法，通过在SemanticKITTI数据集的全景分割排行榜上表现出最领先的性能，证明基于几何的传统聚类算法值得考虑，我们是第一个尝试使用聚类算法进行点云全景分割的研究者，记录了四种具有代表性和实时运行速度的聚类方法的综合技术调查，并将它们实现为Python函数发布，以便其他研究人员使用。

LiDAR全景分割中传统点云聚类方法的技术调研和评估