3D Object Representation Learning : A Set-to-Set Matching Perspective
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 conv...
| Veröffentlicht in: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - 30(2021) vom: 01., Seite 2168-2179 |
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| Weitere Verfasser: | , , |
| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2021
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| Zugriff auf das übergeordnete Werk: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society |
| Schlagworte: | Journal Article |
| Zusammenfassung: | 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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| Beschreibung: | Date Revised 27.01.2021 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
| ISSN: | 1941-0042 |
| DOI: | 10.1109/TIP.2021.3049968 |