SHREC’14 Track: Shape Retrieval of Non-Rigid 3D Human Models


We have created a new benchmarking dataset for testing non-rigid 3D shape retrieval algorithms, one that is much more challenging than existing datasets. Our dataset features exclusively human models, in a variety of body shapes and poses. 3D models of humans are commonly used within computer graphics and vision, and so the ability to distinguish between body shapes is an important shape retrieval problem. In this track nine groups have submitted the results of a total of 22 different methods which have been tested on our new dataset.

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Citation Velocity: 14

Averaging 14 citations per year over the last 3 years.

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Cite this paper

@inproceedings{Pickup2014SHREC14TS, title={SHREC’14 Track: Shape Retrieval of Non-Rigid 3D Human Models}, author={David Pickup and X. Sun and Paul L. Rosin and Ralph R. Martin and Z. Cheng and Zilong Lian and Masahiro Aono and A. Ben Hamza and Alexander M. Bronstein and Umberto Castellani and Stephen Z D Cheng and Valeria Garro and Andrea Giachetti and Afzal Godil and J. Han and Henry Johan and L. Lai and B. Li and C. Li and Hao Li and Roee Litman and X. Liu and Zhiqiang Liu and Yun Lu and Atsushi Tatsuma and Jianbo Ye}, year={2014} }