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Proceedings Paper

A 3D model retrieval approach based on Bayesian networks lightfield descriptor
Author(s): Qinhan Xiao; YanJun Li
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Paper Abstract

A new 3D model retrieval methodology is proposed by exploiting a novel Bayesian networks lightfield descriptor (BNLD). There are two key novelties in our approach: (1) a BN-based method for building lightfield descriptor; and (2) a 3D model retrieval scheme based on the proposed BNLD. To overcome the disadvantages of the existing 3D model retrieval methods, we explore BN for building a new lightfield descriptor. Firstly, 3D model is put into lightfield, about 300 binary-views can be obtained along a sphere, then Fourier descriptors and Zernike moments descriptors can be calculated out from binaryviews. Then shape feature sequence would be learned into a BN model based on BN learning algorithm; Secondly, we propose a new 3D model retrieval method by calculating Kullback-Leibler Divergence (KLD) between BNLDs. Beneficial from the statistical learning, our BNLD is noise robustness as compared to the existing methods. The comparison between our method and the lightfield descriptor-based approach is conducted to demonstrate the effectiveness of our proposed methodology.

Paper Details

Date Published: 2 April 2010
PDF: 9 pages
Proc. SPIE 7651, International Conference on Space Information Technology 2009, 76511Y (2 April 2010); doi: 10.1117/12.855278
Show Author Affiliations
Qinhan Xiao, Northwestern Polytechnical Univ. (China)
YanJun Li, Northwestern Polytechnical Univ. (China)


Published in SPIE Proceedings Vol. 7651:
International Conference on Space Information Technology 2009
Xingrui Ma; Baohua Yang; Ming Li, Editor(s)

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