基于Transformer的城市三角网格语义分割方法OA北大核心CSTPCD

For understanding and analyzing three-dimensional city scenes,semantic segmentation from urban triangle mesh data is a very important method for recognizing objects of different categories.Urban triangle mesh is a spatial three-dimensional geometric data with rich spatial topological relationships,which contains a lot of spatial geometric information.However,existing methods only extract features for each geometric information separately,and simply fuse them for semantic segmentation with difficulty in utilizing the relationship between spatial information,resulting in poor performance in segmenting individual objects of urban triangle mesh data.To solve these problems,we propose a network model UMeT(Urban Mesh Transformer)based on self-attention mechanism Transformer,which contains MLP(Multi-Layer Perceptron)and MeshiT(Mesh in Transformer)module.It not only uses MLP module to extract high-dimensional features,but also uses the MeshiT module to calculate the relationship between various geometric information,effectively mining the hidden relationship in urban triangle mesh data.UMeT extracts high-dimensional features,and ensures spatial invariance of urban triangle mesh data at the same time,improving the accuracy of semantic segmentation.