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Computational Visual Media 2016, 2(3): 231-243 https://doi.org/10.1007/s41095-016-0045-5
Research Article | Open Access | Issue | Published: 14 April 2016

Skeleton-based canonical forms for non-rigid 3D shape retrieval

Show Author's Information David Pickup1( ),  Xianfang Sun1,  Paul L. Rosin1,  Ralph R. Martin1
School of Computer Science and Informatics, Cardiff University, Cardiff, CF24 3AA, UK.
Keywords:
canonical forms, shape retrieval, skeletons, pose invariance
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Pickup D, Sun X, Rosin PL, et al. Skeleton-based canonical forms for non-rigid 3D shape retrieval. Computational Visual Media, 2016, 2(3): 231-243. https://doi.org/10.1007/s41095-016-0045-5
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Abstract Full text About this article

The retrieval of non-rigid 3D shapes is an important task. A common technique is to simplify this problem to a rigid shape retrieval task by producing a bending-invariant canonical form for each shape in the dataset to be searched. It is common for these techniques to attempt to “unbend” a shape by applying multidimensional scaling (MDS) to the distances between points on the mesh, but this leads to unwanted local shape distortions. We instead perform the unbending on the skeleton of the mesh, and use this to drive the deformation of the mesh itself. This leads to computational speed-up, and reduced distortion of local shape detail. We compare our method against other canonical forms: our experiments show that our method achieves state-of-the-art retrieval accuracy in a recent canonical forms benchmark, and only a small drop in retrieval accuracy over the state-of-the-art in a second recent benchmark, while being significantly faster.


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About this article

Skeleton-based canonical forms for non-rigid 3D shape retrieval

Show Author's information David Pickup1( ), Xianfang Sun1, Paul L. Rosin1, Ralph R. Martin1
School of Computer Science and Informatics, Cardiff University, Cardiff, CF24 3AA, UK.

Abstract

The retrieval of non-rigid 3D shapes is an important task. A common technique is to simplify this problem to a rigid shape retrieval task by producing a bending-invariant canonical form for each shape in the dataset to be searched. It is common for these techniques to attempt to “unbend” a shape by applying multidimensional scaling (MDS) to the distances between points on the mesh, but this leads to unwanted local shape distortions. We instead perform the unbending on the skeleton of the mesh, and use this to drive the deformation of the mesh itself. This leads to computational speed-up, and reduced distortion of local shape detail. We compare our method against other canonical forms: our experiments show that our method achieves state-of-the-art retrieval accuracy in a recent canonical forms benchmark, and only a small drop in retrieval accuracy over the state-of-the-art in a second recent benchmark, while being significantly faster.

Keywords: canonical forms, shape retrieval, skeletons, pose invariance

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Publication history

Revised: 26 January 2016
Accepted: 20 February 2016
Published: 14 April 2016
Issue date: September 2016

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© The Author(s) 2016

Acknowledgements

This work was supported by EPSRC Research Grant No. EP/J02211X/1.

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