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Clustering objects from the LiDAR point cloud is an important researchproblem with many applications such as autonomous driving. To meet thereal-time requirement, existing research proposed to apply theconnected-component-labeling (CCL) technique on LiDAR spherical range imagewith a heuristic condition to check if two neighbor points are connected.However, LiDAR range image is different from a binary image which has adeterministic condition to tell if two pixels belong to the same component. Theheuristic condition used on the LiDAR range image only works empirically, whichsuggests the LiDAR clustering algorithm should be robust to potential failuresof the empirical heuristic condition. To overcome this challenge, this paperproposes a divide-and-merge LiDAR clustering algorithm. This algorithm firstlyconducts clustering in each evenly divided local region, then merges the localclustered small components by voting on edge point pairs. Assuming there are$N$ LiDAR points of objects in total with $m$ divided local regions, the timecomplexity of the proposed algorithm is $O(N)+O(m^2)$. A smaller $m$ means thevoting will involve more neighbor points, but the time complexity will becomelarger. So the $m$ controls the trade-off between the time complexity and theclustering accuracy. A proper $m$ helps the proposed algorithm work inreal-time as well as maintain good performance. We evaluate thedivide-and-merge clustering algorithm on the SemanticKITTI panopticsegmentation benchmark by cascading it with a state-of-the-art semanticsegmentation model. The final performance evaluated through the leaderboardachieves the best among all published methods. The proposed algorithm isimplemented with C++ and wrapped as a python function. It can be easily usedwith the modern deep learning framework in python.

A Divide-and-Merge Point Cloud Clustering Algorithm for LiDAR Panoptic Segmentation