A semantic segmentation method for vehicle-borne laser scanning point clouds in motorway scenes

Vehicle-borne laser scanning (VLS) point cloud semantic segmentation is one of the fundamental issues in motorway target extraction. In this study, a uniform sampling-based neural network (NN) is constructed based on the popular RandLA-Net. The uniform sampling can ensure high efficiency and stable spatial coverage. In the proposed NN, a local extra feature encoding structure is designed, and it is combined with local geometric spatial encoding and attention mechanisms to enhance the distinctiveness of depth features and improve the geometric detail preservation ability of the network. The VLS point cloud dataset of motorway scenes collected in this study is labelled for network training and evaluation. Experimental results demonstrate the superiority of the proposed network over the state-of-the-art networks, attaining the highest intersection-over-union (IoU) on the majority of categories and a 4.39% improvement in mean IoU on all categories. For the pole-shaped targets, the improvement in IoU is 9.08% relative to those of the compared methods. The labelled VLS point cloud dataset generated in this work will be made publicly available.