  With the rapid advances of autonomous driving, it becomes critical to equip
its sensing system with more holistic 3D perception. However, existing works
focus on parsing either the objects (e.g. cars and pedestrians) or scenes (e.g.
trees and buildings) from the LiDAR sensor. In this work, we address the task
of LiDAR-based panoptic segmentation, which aims to parse both objects and
scenes in a unified manner. As one of the first endeavors towards this new
challenging task, we propose the Dynamic Shifting Network (DS-Net), which
serves as an effective panoptic segmentation framework in the point cloud
realm. In particular, DS-Net has three appealing properties: 1) strong backbone
design. DS-Net adopts the cylinder convolution that is specifically designed
for LiDAR point clouds. The extracted features are shared by the semantic
branch and the instance branch which operates in a bottom-up clustering style.
2) Dynamic Shifting for complex point distributions. We observe that
commonly-used clustering algorithms like BFS or DBSCAN are incapable of
handling complex autonomous driving scenes with non-uniform point cloud
distributions and varying instance sizes. Thus, we present an efficient
learnable clustering module, dynamic shifting, which adapts kernel functions
on-the-fly for different instances. 3) Consensus-driven Fusion. Finally,
consensus-driven fusion is used to deal with the disagreement between semantic
and instance predictions. To comprehensively evaluate the performance of
LiDAR-based panoptic segmentation, we construct and curate benchmarks from two
large-scale autonomous driving LiDAR datasets, SemanticKITTI and nuScenes.
Extensive experiments demonstrate that our proposed DS-Net achieves superior
accuracies over current state-of-the-art methods. Notably, we achieve 1st place
on the public leaderboard of SemanticKITTI, outperforming 2nd place by 2.6% in
terms of the PQ metric.
