RWAFormer: a lightweight road LiDAR point cloud segmentation network based on transformer

Point cloud semantic segmentation technology for road scenes plays an important role in the field of autonomous driving. However, accurate semantic segmentation of large-scale and non-uniformly dense LiDAR road point clouds still faces severe challenges. To this end, this paper proposes a road point cloud semantic segmentation algorithm called RWAFormer. First, a sparse tensor feature encoding module (STFE) is introduced to enhance the network’s ability to extract local features of point clouds. Secondly, a radial window attention module (RWA) is designed to dynamically select the neighborhood window size according to the distance of the point cloud data from the center point, effectively aggregating the information of long-distance sparse point clouds to the adjacent dense areas, significantly improving the segmentation effect of long-distance point clouds. Experimental results show that our method achieves an average intersection over union (mIoU) of 75.3 and 82.0% on the Semantickitti and Nuscenes datasets, and an accuracy (Acc) of 94.5 and 97.4%. These results validate the effectiveness and superiority of RWAFormer in road point cloud semantic segmentation.