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231225s2021 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2021.3082310
|2 doi
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|a pubmed24n1086.xml
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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 Xu, Yong
|e verfasserin
|4 aut
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| 245 |
1 |
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|a Multi-View 3D Shape Recognition via Correspondence-Aware Deep Learning
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| 264 |
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|c 2021
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| 336 |
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|a Text
|b txt
|2 rdacontent
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| 337 |
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|a ƒaComputermedien
|b c
|2 rdamedia
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| 338 |
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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 Revised 03.06.2021
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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| 520 |
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|a In recent years, multi-view learning has emerged as a promising approach for 3D shape recognition, which identifies a 3D shape based on its 2D views taken from different viewpoints. Usually, the correspondences inside a view or across different views encode the spatial arrangement of object parts and the symmetry of the object, which provide useful geometric cues for recognition. However, such view correspondences have not been explicitly and fully exploited in existing work. In this paper, we propose a correspondence-aware representation (CAR) module, which explicitly finds potential intra-view correspondences and cross-view correspondences via k NN search in semantic space and then aggregates the shape features from the correspondences via learned transforms. Particularly, the spatial relations of correspondences in terms of their viewpoint positions and intra-view locations are taken into account for learning correspondence-aware features. Incorporating the CAR module into a ResNet-18 backbone, we propose an effective deep model called CAR-Net for 3D shape classification and retrieval. Extensive experiments have demonstrated the effectiveness of the CAR module as well as the excellent performance of the CAR-Net
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| 650 |
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|a Journal Article
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| 700 |
1 |
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|a Zheng, Chaoda
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Xu, Ruotao
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Quan, Yuhui
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Ling, Haibin
|e verfasserin
|4 aut
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| 773 |
0 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 30(2021) vom: 01., Seite 5299-5312
|w (DE-627)NLM09821456X
|x 1941-0042
|7 nnns
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| 773 |
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|g volume:30
|g year:2021
|g day:01
|g pages:5299-5312
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|u http://dx.doi.org/10.1109/TIP.2021.3082310
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