Journal
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
Volume 29, Issue 3, Pages 868-880Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCSVT.2018.2810191
Keywords
3D Object Retrieval; Graph-Based Model; Latent Variable Model; Multi-View
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Funding
- National Natural Science Foundation of China [61772359, 61472275, 61502337, 61572356]
- Tianjin Research Program of Application Foundation and Advanced Technology [15JCYBJC16200]
- China Scholarship Council [201506255073]
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View-based 3D object retrieval, in which multiple views are used for representation and retrieval, has attracted increasing attention due to its great flexibility. In this paper, we propose a discriminative multi-view latent variable model (MVLVM) for this task. Specifically, we design MVLVM to have an undirected graph structure in which the view set of a given 3D object is treated as the observations from which to discover the latent visual and spatial contexts. Then, we detail the learning and inference process of MVLVM for view-based 3D object retrieval. The proposed MVLVM has the following beneficial features: 1) it jointly learns visual and spatial contexts for 3D object modelling and 2) it avoids the difficulty of representative view extraction for model representation. Consequently, it can support flexible 3D model retrieval for real applications by avoiding camera array constraints, which severely constrain traditional methods. We report extensive experiments conducted on single-modal datasets (the NTU and ITI datasets) and a multi-modal dataset (MVRED-RGB and MVRED-Depth). These comparative experiments demonstrate the superiority of the proposed method.
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