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Feature-preserved 3D Canonical Form

Published

Author(s)

Afzal A. Godil, Zhouhui Lian

Abstract

Abstract Measuring the dissimilarity between non-rigid objects is a challenging problem in 3D shape retrieval. One potential solution is to construct the models’ 3D canonical forms (i.e., isometry-invariant representations in 3D Euclidean domain) on which any rigid shape matching algorithm can be applied. However, existing methods, which are typically based on embedding procedures, result in greatly distorted canonical forms, and thus could not provide satisfactory performance to distinguish non-rigid models. In this paper, we present a feature-preserved canonical form for non-rigid 3D watertight meshes. The basic idea is to naturally deform original models against corresponding initial canonical forms calculated by Multidimensional Scaling (MDS). Specifically, objects are first segmented into near-rigid subparts, and then, through properly-designed rotations and translations, original subparts are transformed into poses that correspond well with their positions and directions on MDS canonical forms. Final results are obtained by solving nonlinearminimization problems for optimal alignments and smoothing boundaries between subparts. Experiments on a widely-utilized non-rigid 3D shape benchmark not only clearly verify the advantages of our algorithmagainst existing approaches, but also demonstrate that, with the help of the proposed canonical form, we can obtain significantly better retrieval accuracy compared to the state-of-the-art.
Citation
International Journal of Computer Vision

Keywords

Canonical Form · Multidimensional Scaling · Canonical Form, Multidimensional Scaling, 3D Shape Retrieval, Non-rigid

Citation

Godil, A. and Lian, Z. (2012), Feature-preserved 3D Canonical Form, International Journal of Computer Vision, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=911523 (Accessed May 21, 2024)

Issues

If you have any questions about this publication or are having problems accessing it, please contact reflib@nist.gov.

Created July 28, 2012, Updated February 19, 2017