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231225s2021 xx |||||o 00| ||eng c |
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|a 10.1109/TIP.2021.3049968
|2 doi
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|a pubmed24n1067.xml
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|a eng
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|a Yu, Tan
|e verfasserin
|4 aut
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|a 3D Object Representation Learning
|b A Set-to-Set Matching Perspective
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|c 2021
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Revised 27.01.2021
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a In this paper, we tackle the 3D object representation learning from the perspective of set-to-set matching. Given two 3D objects, calculating their similarity is formulated as the problem of set-to-set similarity measurement between two set of local patches. As local convolutional features from convolutional feature maps are natural representations of local patches, the set-to-set matching between sets of local patches is further converted into a local features pooling problem. To highlight good matchings and suppress the bad ones, we exploit two pooling methods: 1) bilinear pooling and 2) VLAD pooling. We analyze their effectiveness in enhancing the set-to-set matching and meanwhile establish their connection. Moreover, to balance different components inherent in a bilinear-pooled feature, we propose the harmonized bilinear pooling operation, which follows the spirits of intra-normalization used in VLAD pooling. To achieve an end-to-end trainable framework, we implement the proposed harmonized bilinear pooling and intra-normalized VLAD as two layers to construct two types of neural network, multi-view harmonized bilinear network (MHBN) and multi-view VLAD network (MVLADN). Systematic experiments conducted on two public benchmark datasets demonstrate the efficacy of the proposed MHBN and MVLADN in 3D object recognition
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|a Journal Article
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|a Meng, Jingjing
|e verfasserin
|4 aut
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| 700 |
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|a Yang, Ming
|e verfasserin
|4 aut
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| 700 |
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|a Yuan, Junsong
|e verfasserin
|4 aut
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| 773 |
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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 2168-2179
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|g volume:30
|g year:2021
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|u http://dx.doi.org/10.1109/TIP.2021.3049968
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