SectorGSnet: Sector Learning for Efficient Ground Segmentation of Outdoor LiDAR Point Clouds

Abstract

Ground segmentation of outdoor LiDAR point clouds remains challenging due to its sparse and irregular nature. This paper presents SectorGSnet: a ground segmentation framework for outdoor LiDAR point clouds, aiming to accomplish this task efficiently and effectively. The framework consists of a sector encoder module and a segmentation module. The former module introduces a novel bird's-eye-view (BEV) sector partition strategy that discretizes the point cloud into sectors of varying sizes to enhance the 2D representation ability of the point cloud. Then, the points within each sector are fed into a multimodal PointNet encoder to obtain the corresponding sector feature map. In the latter module, a lightweight segmentation network next learns binary classification from the sector feature map, and point labels are restored from the sector segmentation results. Our proposed framework is trained and evaluated on SemanticKITTI against state-of-the-art 2D projection-based approaches and achieves an excellent balance between performance and computational complexity. We conducted inference at 170.6 Hz on a desktop PC with a GTX2080Ti GPU, and also experimented on a resource-limited platform with only 10 watts of power.