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

A Technical Survey and Evaluation of Traditional Point Cloud Clustering Methods for LiDAR Panoptic Segmentation