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.

翻译：可靠的激光雷达全景分割（LPS），包含语义分割与实例分割，对于众多机器人应用（如自动驾驶）至关重要。本文提出一种名为PANet的新型LPS框架，旨在消除对偏移分支的依赖，并提升对大型物体的分割性能（此类物体常因聚类算法而产生过分割）。首先，我们提出一种基于“采样-偏移-分组”方案的非学习型稀疏实例提议（SIP）模块，能够高效地将原始点云中的物体点直接分组为实例。具体而言，引入平衡点采样以生成稀疏种子点，使点在距离范围内分布更均匀；同时提出一种名为气泡偏移的偏移模块，将种子点收缩至聚类中心。随后，利用连通分量标记算法生成实例提议。此外，设计实例聚合模块以整合可能存在的碎片化实例，从而提升SIP模块在大型物体上的性能。大量实验表明，PANet在SemanticKITTI验证集和nuScenes验证集的全景分割任务中，达到了已发表工作中的最优性能。