  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. 2) Dynamic Shifting for complex point distributions. We
observe that commonly-used clustering algorithms 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) Extension to 4D prediction. Furthermore, we extend
DS-Net to 4D panoptic LiDAR segmentation by the temporally unified instance
clustering on aligned LiDAR frames. 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 in both tasks. Notably, in the
single frame version of the task, we outperform the SOTA method by 1.8% in
terms of the PQ metric. In the 4D version of the task, we surpass 2nd place by
5.4% in terms of the LSTQ metric.
