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A multiresolution descriptor for deformable 3D shape retrieval

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Abstract

In this paper, we present a spectral graph wavelet framework for the analysis and design of efficient shape signatures for nonrigid 3D shape retrieval. Although this work focuses primarily on shape retrieval, our approach is, however, fairly general and can be used to address other 3D shape analysis problems. In a bid to capture the global and local geometry of 3D shapes, we propose a multiresolution signature via a cubic spline wavelet generating kernel. The parameters of the proposed signature can be easily determined as a trade-off between effectiveness and compactness. Experimental results on two standard 3D shape benchmarks demonstrate the much better performance of the proposed shape retrieval approach in comparison with three state-of-the-art methods. Additionally, our approach yields a higher retrieval accuracy when used in conjunction with the intrinsic spatial partition matching.

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Acknowledgements

This work was supported in part by NSERC Discovery Grant.

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Authors and Affiliations

  1. Concordia Institute for Information Systems Engineering, Concordia University, Montreal, QC, Canada

    Chunyuan Li & A. Ben Hamza

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  1. Chunyuan Li
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  2. A. Ben Hamza
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Correspondence to A. Ben Hamza.

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Li, C., Ben Hamza, A. A multiresolution descriptor for deformable 3D shape retrieval. Vis Comput 29, 513–524 (2013). https://doi.org/10.1007/s00371-013-0815-3

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