LiDAR panoptic segmentation facilitates an autonomous vehicle to comprehensively understand the surrounding objects and scenes and is required to run in real time. The recent proposal-free methods accelerate the algorithm, but their effectiveness and efficiency are still limited owing to the difficulty of modeling non-existent instance centers and the costly center-based clustering modules. To achieve accurate and real-time LiDAR panoptic segmentation, a novel center focusing network (CFNet) is introduced. Specifically, the center focusing feature encoding (CFFE) is proposed to explicitly understand the relationships between the original LiDAR points and virtual instance centers by shifting the LiDAR points and filling in the center points. Moreover, to leverage the redundantly detected centers, a fast center deduplication module (CDM) is proposed to select only one center for each instance. Experiments on the SemanticKITTI and nuScenes panoptic segmentation benchmarks demonstrate that our CFNet outperforms all existing methods by a large margin and is 1.6 times faster than the most efficient method. The code is available at https://github.com/GangZhang842/CFNet.

翻译：激光雷达全景分割有助于自动驾驶车辆全面理解周围物体和场景，并且需要实时运行。近期无提案方法加速了算法，但由于难以对不存在的实例中心建模以及基于中心的聚类模块成本高昂，其有效性和效率仍受限制。为实现精确且实时的激光雷达全景分割，本文提出了一种新颖的中心聚焦网络（CFNet）。具体而言，本文提出了中心聚焦特征编码（CFFE），通过移动激光雷达点并填充中心点，显式理解原始激光雷达点与虚拟实例中心之间的关系。此外，为利用冗余检测到的中心，本文设计了快速中心去重模块（CDM），为每个实例仅选择一个中心。在SemanticKITTI和nuScenes全景分割基准上的实验表明，我们的CFNet以大幅优势优于所有现有方法，且比最高效的方法快1.6倍。代码开源于https://github.com/GangZhang842/CFNet。