Point cloud semantic segmentation based on range view representation and point-wise refinement
3D point cloud semantic segmentation is an important way for machines to achieve environment awareness.In exist-ing research,voxel-based methods and point-based methods are computationally infficient in the face of large-scale point cloud data.And range-based methods inevitably cause accuracy loss when projecting and back projecting point clouds.To solve the above problems,this paper proposed RPNet,a noval 3D point cloud semantic segmentation framework based on range view repre-sentation and point-wise refinement.Sufficient experiments show that the proposed method achieves a mIoU of 64.2%and an infer-ence speed of 58 frames per second on the 3D point cloud outdoor scene dataset SemanticKITTI,balacing high accuracy and speed.
semantic segmentation3D point cloudrange viewpoint-wise refinement
NETL
NSTL
万方数据
维普
