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Harmonic mean normalized Laplace–Beltrami spectral descriptor

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Abstract

This paper proposes a framework based on harmonic mean normalized Laplace–Beltrami spectral descriptor. A series of experiments show that the harmonic mean normalization has better performance for non-rigid 3D retrieval, and it is robust to holes, local scaling, noise and sampling. To better distinguish shapes with fine or rough details, weighting method and fusion method are also employed. Weighting method reduces the negative impact of high-frequency information, and fusion method combines multi-level spectral information in both low and high frequencies. Our approach has better performance than other state-of-the-art methods on both retrieval accuracy and time consumption for stretched non-rigid 3D shapes.

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Acknowledgments

This work was partially supported by the National Natural Science Foundation of China (61173103, 61572099, 61320106008, 91230103, 61363048, 61262050, 61402300), National Science and Technology Major Project (2013ZX04005021, 2014ZX04001011), the Natural Science Foundation of Hebei Province (F2014210127), the Funded Projects for Introduction of Overseas Scholars of Hebei Province, and the Funds for Excellent Young Scholar of Shijiazhuang Tiedao University.

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Authors and Affiliations

  1. School of Mathematical Sciences, Dalian University of Technology, Dalian, 116024, China

    Yusong Liu, Zhixun Su & Junjie Cao

  2. School of Science, China University of Petroleum (Huadong), Qingdao, 266555, China

    Yusong Liu

  3. School of Information Science and Technology, Shijiazhuang Tiedao University, Shijiazhuang, 050043, China

    Hui Wang

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  1. Yusong Liu
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Correspondence to Zhixun Su.

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Liu, Y., Su, Z., Cao, J. et al. Harmonic mean normalized Laplace–Beltrami spectral descriptor. Vis Comput 32, 1097–1108 (2016). https://doi.org/10.1007/s00371-015-1172-1

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