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|a 10.1109/TPAMI.2022.3221785
|2 doi
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|a pubmed24n1162.xml
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|a (DE-627)NLM348883803
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|a (NLM)36374885
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|a DE-627
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|e rakwb
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|a eng
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| 100 |
1 |
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|a Wei, Xin
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Learning View-Based Graph Convolutional Network for Multi-View 3D Shape Analysis
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|c 2023
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|a Text
|b txt
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|a ƒaComputermedien
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|2 rdamedia
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|a ƒa Online-Ressource
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|2 rdacarrier
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| 500 |
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|a Date Completed 07.05.2023
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|a Date Revised 07.05.2023
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a View-based approach that recognizes 3D shape through its projected 2D images has achieved state-of-the-art results for 3D shape recognition. The major challenges are how to aggregate multi-view features and deal with 3D shapes in arbitrary poses. We propose two versions of a novel view-based Graph Convolutional Network, dubbed view-GCN and view-GCN++, to recognize 3D shape based on graph representation of multiple views. We first construct view-graph with multiple views as graph nodes, then design two graph convolutional networks over the view-graph to hierarchically learn discriminative shape descriptor considering relations of multiple views. Specifically, view-GCN is a hierarchical network based on two pivotal operations, i.e., feature transform based on local positional and non-local graph convolution, and graph coarsening based on a selective view-sampling operation. To deal with rotation sensitivity, we further propose view-GCN++ with local attentional graph convolution operation and rotation robust view-sampling operation for graph coarsening. By these designs, view-GCN++ achieves invariance to transformations under the finite subgroup of rotation group SO(3). Extensive experiments on benchmark datasets (i.e., ModelNet40, ScanObjectNN, RGBD and ShapeNet Core55) show that view-GCN and view-GCN++ achieve state-of-the-art results for 3D shape classification and retrieval tasks under aligned and rotated settings
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|a Journal Article
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| 700 |
1 |
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|a Yu, Ruixuan
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Sun, Jian
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on pattern analysis and machine intelligence
|d 1979
|g 45(2023), 6 vom: 25. Juni, Seite 7525-7541
|w (DE-627)NLM098212257
|x 1939-3539
|7 nnns
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| 773 |
1 |
8 |
|g volume:45
|g year:2023
|g number:6
|g day:25
|g month:06
|g pages:7525-7541
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| 856 |
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|u http://dx.doi.org/10.1109/TPAMI.2022.3221785
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|d 45
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