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

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.

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Correspondence to David Pickup.

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This article is published with open access at Springerlink.com

David Pickup obtained his M.Eng. degree from the University of Bristol and Ph.D. degree from the University of Bath. He is currently a postdoctoral research associate in the Visual Computing research group, Cardiff University. His current research centres on three-dimensional shape retrieval.

Xianfang Sun received his Ph.D. from the Institute of Automation, Chinese Academy of Sciences, in 1994. He is currently a senior lecturer in the School of Computer Science & Informatics, Cardiff University, Wales, UK. His main research interests include computer vision, computer graphics, pattern recognition, and artificial intelligence.

Paul L. Rosin is a professor in the School of Computer Science & Informatics, Cardiff University. Previous posts include lecturer in the Department of Information Systems and Computing at Brunel University London, UK, research scientist in the Institute for Remote Sensing Applications at Joint Research Centre, Ispra, Italy, and lecturer at Curtin University of Technology, Perth, Australia. His research interests include the representation, segmentation, and grouping of curves, knowledge-based vision systems, early image representations, low level image processing, machine vision approaches to remote sensing, methods for evaluation of approximation algorithms, etc., medical and biological image analysis, mesh processing, non-photorealistic rendering, and the analysis of shape in art and architecture.

Ralph R. Martin obtained his Ph.D. degree in 1983 from Cambridge University. Since then he has been at Cardiff University, where he now holds a Chair and leads the Visual Computing research group. He is also a guest professor at Tsinghua and two other universities in China, and is a director of Scientific Programmes of the One Wales Research Institute of Visual Computing. His publications include about 300 papers and 15 books covering such topics as solid modelling, surface modelling, reverse engineering, intelligent sketch input, mesh processing, video processing, computer graphics, vision based geometric inspection, and geometric reasoning. He is a Fellow of the Learned Society of Wales, the Institute of Mathematics and its Applications, and the British Computer Society. He is on the editorial boards of Computer-Aided Design, Computer Aided Geometric Design, Geometric Models, and Computational Visual Media.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0), which permits use, duplication, adaptation, distribution, and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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Pickup, D., Sun, X., Rosin, P.L. et al. Skeleton-based canonical forms for non-rigid 3D shape retrieval. Comp. Visual Media 2, 231–243 (2016). https://doi.org/10.1007/s41095-016-0045-5

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Keywords

  • canonical forms
  • shape retrieval
  • skeletons
  • pose invariance