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3D model retrieval using Bag-of-View-Words

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Abstract

The view-based 3D model descriptors, which represent a 3D model using its projected views, have limitations on viewpoints sampling and computational cost. This paper proposes a new 3D model descriptor, called the Bag-of-View-Words (BoVW) descriptor, which describes a 3D model by measuring the occurrences of its projected views. An adaptive clustering method is applied to reduce the redundancy of the projected views of each 3D model. A 3D model is represented by a multi-resolution histogram, which is combined by several BoVW descriptors at different levels. The codebook is obtained by unsupervised learning. We also propose a new pyramid matching method for 3D model comparison. Experimental results demonstrated that our method outperforms several existing 3D model descriptors in respect of retrieval precision and computational cost.

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Acknowledgements

The authors would like to thank Bo Li for providing the contest results of the SHREC2012 Generic 3D model track, and Professor Greg Hamerly for providing the source code of G-means. The authors would like to thank the reviewers and the editor for their time and valuable comments.

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  1. The Chinese University of Hong Kong, Room 106, MMW Engineering Building, Shatin, Hong Kong

    Ke Ding, Wei Wang & Yunhui Liu

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  1. Ke Ding
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  2. Wei Wang
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  3. Yunhui Liu
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Correspondence to Ke Ding.

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Ding, K., Wang, W. & Liu, Y. 3D model retrieval using Bag-of-View-Words. Multimed Tools Appl 72, 2701–2722 (2014). https://doi.org/10.1007/s11042-013-1560-3

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