期刊
IEEE ACCESS
卷 8, 期 -, 页码 205181-205189出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2020.3037236
关键词
Three-dimensional displays; Feature extraction; Shape; Topology; Data mining; Face recognition; Surface reconstruction; Algebraic topology reconstruction; effectively feature extracting method; graph convolutional method; mesh recognition
Three-dimensional polygon mesh recognition has a significant impact on current computer graphics. However, its application to some real-life fields, such as unmanned driving and medical image processing, has been restricted due to the lack of inner-interactivity, shift-invariance, and numerical uncertainty of mesh surfaces. In this paper, an interconnected topological dual graph that extracts adjacent information from each triangular face of a polygon mesh is constructed, in order to address the above issues. On the basis of the algebraic topological graph, we propose a mesh graph neural network, called MeshGraphNet, to effectively extract features from mesh data. In this concept, the graph node-unit and correlation between every two dual graph vertexes are defined, the concept of aggregating features extracted from geodesically adjacent nodes is introduced, and a graph neural network with available and effective blocks is proposed. With these methods, MeshGraphNet performs well in 3D shape representation by avoiding the lack of inner-interactivity, shift-invariance, and the numerical uncertainty problems of mesh data. We conduct extensive 3D shape classification experiments and provide visualizations of the features extracted from the fully connected layers. The results demonstrate that our method performs better than state-of-the-art methods and improves the recognition accuracy by 4-4.5%.
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