As a rising task, panoptic segmentation is faced with challenges in both semantic segmentation and instance segmentation. However, in terms of speed and accuracy, existing LiDAR methods in the field are still limited. In this paper, we propose a fast and high-performance LiDAR-based framework, referred to as Panoptic-PHNet, with three attractive aspects: 1) We introduce a clustering pseudo heatmap as a new paradigm, which, followed by a center grouping module, yields instance centers for efficient clustering without object-level learning tasks. 2) A knn-transformer module is proposed to model the interaction among foreground points for accurate offset regression. 3) For backbone design, we fuse the fine-grained voxel features and the 2D Bird's Eye View (BEV) features with different receptive fields to utilize both detailed and global information. Extensive experiments on both SemanticKITTI dataset and nuScenes dataset show that our Panoptic-PHNet surpasses state-of-the-art methods by remarkable margins with a real-time speed. We achieve the 1st place on the public leaderboard of SemanticKITTI and leading performance on the recently released leaderboard of nuScenes.

本文提出了一种快速高效的基于LiDAR的Panoptic-PHNet框架，其引入簇伪热图作为新的范例，提出了knn-transformer模块来精确回归前景点之间的相互作用，并将细粒度体素特征与不同感受野的2D俯瞰视图（BEV）特征融合，通过在SemanticKITTI数据集和nuScenes数据集上的大量实验证明Panoptic-PHNet超过了最先进方法，在真实时间内实现了极高的性能。

Panoptic-PHNet: 通过聚类伪热度图实现实时高精度LiDAR全景分割