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Deep shape-aware descriptor for nonrigid 3D object retrieval

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Abstract

Deep learning is a rapidly growing discipline that models high-level features in data as multilayered neural networks. In this paper, we propose a deep learning approach for 3D shape retrieval using a multi-level feature learning methodology. We first extract low-level features or local descriptors from a 3D shape using spectral graph wavelets. Then, we construct mid-level features from these local descriptors via the bag-of-features paradigm by employing locality-constrained linear coding as a feature coding method, together with the biharmonic distance as a measure of the spatial relationship between each pair of bag-of-feature descriptors. Finally, high-level shape features are learned via a deep auto-encoder, resulting in a deep shape-aware descriptor that is compact, geometrically informative and efficient to compute. The proposed 3D shape retrieval approach is evaluated on SHREC-2014 and SHREC-2015 datasets through extensive experiments, and the results show compelling superiority of our approach over the state-of-the-art methods.

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Notes

  1. ARPACK (ARnoldi PACKage) is a MATLAB library for computing the eigenvalues and eigenvectors of large matrices.

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Authors and Affiliations

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

    Hamed Ghodrati & A. Ben Hamza

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  1. Hamed Ghodrati
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  2. A. Ben Hamza
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Correspondence to A. Ben Hamza.

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Ghodrati, H., Ben Hamza, A. Deep shape-aware descriptor for nonrigid 3D object retrieval. Int J Multimed Info Retr 5, 151–164 (2016). https://doi.org/10.1007/s13735-016-0103-x

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