A deep-learning model for semantic segmentation of meshes from UAV oblique images

Though mesh generated by UAV-based oblique photogrammetry has revolutionized the large urban scene data representation, its semantic segmentation still challenging due to structural irregularities. In this paper, a deep learning model is proposed for the mesh semantic segmentation. To efficiently capture local geometry and neighbourhood context within the mesh, graph concept and transformer blocks are employed for mesh representation and feature learning. Based on these components, a hierarchical network architecture is presented. Experimental results demonstrate that the proposed network performs well on the self-made Wuhan dataset. The ablation study shows the improvement brought by each component in the network. By comparison, both mIoU and mF1 of our network are much higher than the prior work on the public SUM dataset, achieving 83.7% and 73.7%.