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

Transformer based urban triangle mesh semantic segmentation method

中文摘要英文摘要

对城市三角网格(Urban Triangle Mesh)数据进行语义分割以识别不同类别的物体,是理解和分析三维城市场景的一种非常重要的方法.城市三角网格是一种具有丰富空间拓扑关系的三维空间几何数据,包含大量的几何信息,然而,现有的方法仅仅单独对每种几何信息进行特征提取,然后简单地融合再进行语义分割,难以利用几何信息之间的关联性,对个别物体的分割性能不佳.为了解决上述问题,提出一种基于自注意力机制Transformer的模型UMeT(Urban Mesh Transformer),其由多层感知机和MeshiT(Mesh in Transformer)模块构成,不仅可以利用多层感知机提取高维特征,还可以利用MeshiT模块计算各种几何信息之间的关联性,有效挖掘城市三角网格数据中隐含的关联.实验证明,UMeT能提取高维特征,同时保证城市三角网格数据的空间不变性,从而提升了语义分割的准确性.

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.

资文杰;贾庆仁;陈浩;李军;景宁

国防科技大学电子科学学院,长沙,410073国防科技大学电子科学学院,长沙,410073||自然资源部南方丘陵区自然资源监测监管重点实验室,长沙,410073

计算机与自动化

城市三角网格语义分割Transformermesh自注意力机制

urban triangle mesh datasemantic segmentationTransformermeshself-attention mechanism

《南京大学学报(自然科学版)》 2024 (001)

18-25 / 8

国家自然科学基金(U19A2058,62106276,42101435),湖南省自然科学基金(2021JJ40667)

10.13232/j.cnki.jnju.2024.01.003

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