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Comparing local shape descriptors

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Abstract

Local shape descriptors can be used for a variety of tasks, from registration to comparison to shape analysis and retrieval. There have been a variety of local shape descriptors developed for these tasks, which have been evaluated in isolation or in pairs, but not against each other. We provide a survey of existing descriptors and a framework for comparing them. We perform a detailed evaluation of the descriptors using real data sets from a variety of sources. We first evaluate how stable these metrics are under changes in mesh resolution, noise, and smoothing. We then analyze the discriminatory ability of the descriptors for the task of shape matching. Finally, we compare the descriptors on a shape classification task. Our conclusion is that sampling the normal distribution and the mean curvature, using 25 samples, and reducing this data to 5–10 samples via Principal Components Analysis, provides robustness to noise and the best shape discrimination results. For shape classification, mean curvature sampled at the vertex or averaged, and the more global Shape Diameter Function, performed the best.

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Acknowledgements

Funded in part by National Science Foundation grants CCF 0702662, DBI 1053171, DMS 0540701, NIH T90 DA022871, Shandong Taishan Fund, NNSF of China, Ministry of Education, People’s Republic of China (985 & 211), Shandong University, and the EU CILIA Project. Thanks to Dr. Bayly for the ferret brains, Dr. Daniel Low for the mandibles, and Dr. Crisco for the radius and ulna bones.

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  1. Paul Heider

    Present address: Google, Mountain View, USA

Authors and Affiliations

  1. Washington University in St. Louis, St. Louis, USA

    Cindy Grimm

  2. Virginia Tech, Blacksburg, USA

    Paul Heider, Alain Pierre-Pierre, Ruosi Li & Rolf Mueller

  3. Facebook, Palo Alto, USA

    Ruosi Li

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  1. Paul Heider
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  2. Alain Pierre-Pierre
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  5. Cindy Grimm
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Correspondence to Cindy Grimm.

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Heider, P., Pierre-Pierre, A., Li, R. et al. Comparing local shape descriptors. Vis Comput 28, 919–929 (2012). https://doi.org/10.1007/s00371-012-0725-9

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