  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.
