Mesh Neural Networks Based on Dual Graph Pyramids

Mesh Neural Networks Based on Dual Graph Pyramids

Deep neural networks (DNNs) have been widely used for mesh processing in recent years. However, current DNNs can not process arbitrary meshes efficiently. On the one hand, most DNNs expect 2-manifold, watertight meshes, but many meshes, whether manually designed or automatically generated, may have...

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Veröffentlicht in:IEEE transactions on visualization and computer graphics. - 1996. - 30(2024), 7 vom: 05. Juni, Seite 4211-4224
1. Verfasser: Li, Xiang-Li (VerfasserIn)
Weitere Verfasser: Liu, Zheng-Ning, Chen, Tuo, Mu, Tai-Jiang, Martin, Ralph R, Hu, Shi-Min
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2024
Zugriff auf das übergeordnete Werk:IEEE transactions on visualization and computer graphics
Schlagworte:Journal Article
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520 |a Deep neural networks (DNNs) have been widely used for mesh processing in recent years. However, current DNNs can not process arbitrary meshes efficiently. On the one hand, most DNNs expect 2-manifold, watertight meshes, but many meshes, whether manually designed or automatically generated, may have gaps, non-manifold geometry, or other defects. On the other hand, the irregular structure of meshes also brings challenges to building hierarchical structures and aggregating local geometric information, which is critical to conduct DNNs. In this paper, we present DGNet, an efficient, effective and generic deep neural mesh processing network based on dual graph pyramids; it can handle arbitrary meshes. First, we construct dual graph pyramids for meshes to guide feature propagation between hierarchical levels for both downsampling and upsampling. Second, we propose a novel convolution to aggregate local features on the proposed hierarchical graphs. By utilizing both geodesic neighbors and euclidean neighbors, the network enables feature aggregation both within local surface patches and between isolated mesh components. Experimental results demonstrate that DGNet can be applied to both shape analysis and large-scale scene understanding. Furthermore, it achieves superior performance on various benchmarks, including ShapeNetCore, HumanBody, ScanNet and Matterport3D. Code and models will be available at https://github.com/li-xl/DGNet 
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700 1 |a Liu, Zheng-Ning  |e verfasserin  |4 aut 
700 1 |a Chen, Tuo  |e verfasserin  |4 aut 
700 1 |a Mu, Tai-Jiang  |e verfasserin  |4 aut 
700 1 |a Martin, Ralph R  |e verfasserin  |4 aut 
700 1 |a Hu, Shi-Min  |e verfasserin  |4 aut 
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