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Hybrid shape descriptor and meta similarity generation for non-rigid and partial 3D model retrieval

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Abstract

Non-rigid and partial 3D model retrieval are two significant and challenging research directions in the field of 3D model retrieval. Little work has been done in proposing a hybrid shape descriptor that works for both retrieval scenarios, let alone the integration of the component features of the hybrid shape descriptor in an automatic way. In this paper, we propose a hybrid shape descriptor that integrates both geodesic distance-based global features and curvature-based local features. We also develop an automatic algorithm to generate meta similarity resulting from different component features of the hybrid shape descriptor based on Particle Swarm Optimization. Experimental results demonstrate the effectiveness and advantages of our framework, as well as the significant improvements in retrieval performances. The framework is general and can be applied to similar approaches that integrate more features for the development of a single algorithm for both non-rigid and partial 3D model retrieval.

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Authors and Affiliations

  1. National Institute of Standards and Technology, Gaithersburg, MD, 20899, USA

    Bo Li & Afzal Godil

  2. Fraunhofer IDM@NTU, 50 Nanyang Avenue, Singapore, Singapore

    Henry Johan

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  1. Bo Li
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  2. Afzal Godil
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Correspondence to Bo Li.

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Li, B., Godil, A. & Johan, H. Hybrid shape descriptor and meta similarity generation for non-rigid and partial 3D model retrieval. Multimed Tools Appl 72, 1531–1560 (2014). https://doi.org/10.1007/s11042-013-1464-2

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