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 this https URL

为了实现准确且实时的 LiDAR 全景分割，引入了一种新的中心聚焦网络（CFNet），其中包括了中心聚焦特征编码（CFFE）和快速中心去重模块（CDM），在 SemanticKITTI 和 nuScenes 全景分割基准测试中性能优于现有方法，并且比效率最高的方法快 1.6 倍。