A 3D model retrieval approach based on Bayesian networks lightfield descriptor

Qinhan Xiao; YanJun Li

doi:10.1117/12.855278

2 April 2010 A 3D model retrieval approach based on Bayesian networks lightfield descriptor

Qinhan Xiao, YanJun Li

Proceedings Volume 7651, International Conference on Space Information Technology 2009; 76511Y (2010) https://doi.org/10.1117/12.855278
Event: International Conference on Space Information Technology 2009, 2009, Beijing, China

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.

Citation Download Citation

Qinhan Xiao and YanJun Li "A 3D model retrieval approach based on Bayesian networks lightfield descriptor", Proc. SPIE 7651, International Conference on Space Information Technology 2009, 76511Y (2 April 2010); https://doi.org/10.1117/12.855278

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