Toward Fine-Grained Sketch-Based 3D Shape Retrieval

In this paper we study, for the first time, the problem of fine-grained sketch-based 3D shape retrieval. We advocate the use of sketches as a fine-grained input modality to retrieve 3D shapes at instance-level - e.g., given a sketch of a chair, we set out to retrieve a specific chair from a gallery...

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Veröffentlicht in:IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - 30(2021) vom: 14., Seite 8595-8606
1. Verfasser: Qi, Anran (VerfasserIn)
Weitere Verfasser: Gryaditskaya, Yulia, Song, Jifei, Yang, Yongxin, Qi, Yonggang, Hospedales, Timothy M, Xiang, Tao, Song, Yi-Zhe
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2021
Zugriff auf das übergeordnete Werk:IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Schlagworte:Journal Article
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520 |a In this paper we study, for the first time, the problem of fine-grained sketch-based 3D shape retrieval. We advocate the use of sketches as a fine-grained input modality to retrieve 3D shapes at instance-level - e.g., given a sketch of a chair, we set out to retrieve a specific chair from a gallery of all chairs. Fine-grained sketch-based 3D shape retrieval (FG-SBSR) has not been possible till now due to a lack of datasets that exhibit one-to-one sketch-3D correspondences. The first key contribution of this paper is two new datasets, consisting a total of 4,680 sketch-3D pairings from two object categories. Even with the datasets, FG-SBSR is still highly challenging because (i) the inherent domain gap between 2D sketch and 3D shape is large, and (ii) retrieval needs to be conducted at the instance level instead of the coarse category level matching as in traditional SBSR. Thus, the second contribution of the paper is the first cross-modal deep embedding model for FG-SBSR, which specifically tackles the unique challenges presented by this new problem. Core to the deep embedding model is a novel cross-modal view attention module which automatically computes the optimal combination of 2D projections of a 3D shape given a query sketch 
650 4 |a Journal Article 
700 1 |a Gryaditskaya, Yulia  |e verfasserin  |4 aut 
700 1 |a Song, Jifei  |e verfasserin  |4 aut 
700 1 |a Yang, Yongxin  |e verfasserin  |4 aut 
700 1 |a Qi, Yonggang  |e verfasserin  |4 aut 
700 1 |a Hospedales, Timothy M  |e verfasserin  |4 aut 
700 1 |a Xiang, Tao  |e verfasserin  |4 aut 
700 1 |a Song, Yi-Zhe  |e verfasserin  |4 aut 
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