Center Focusing Network for Real-Time LiDAR Panoptic Segmentation
Xiaoyan Li Gang Zhang Boyue Wang Yongli Hu Baocai Yin
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.