  Reliable LiDAR panoptic segmentation (LPS), including both semantic and
instance segmentation, is vital for many robotic applications, such as
autonomous driving. This work proposes a new LPS framework named PANet to
eliminate the dependency on the offset branch and improve the performance on
large objects, which are always over-segmented by clustering algorithms.
Firstly, we propose a non-learning Sparse Instance Proposal (SIP) module with
the ``sampling-shifting-grouping" scheme to directly group thing points into
instances from the raw point cloud efficiently. More specifically, balanced
point sampling is introduced to generate sparse seed points with more uniform
point distribution over the distance range. And a shift module, termed bubble
shifting, is proposed to shrink the seed points to the clustered centers. Then
we utilize the connected component label algorithm to generate instance
proposals. Furthermore, an instance aggregation module is devised to integrate
potentially fragmented instances, improving the performance of the SIP module
on large objects. Extensive experiments show that PANet achieves
state-of-the-art performance among published works on the SemanticKITII
validation and nuScenes validation for the panoptic segmentation task.
