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Shape retrieval of non-rigid 3D human models

Pickup, David ORCID:, Sun, Xianfang ORCID:, Rosin, Paul L. ORCID:, Martin, Ralph Robert, Cheng, Z., Lian, Z., Aono, M., Ben Hamza, A., Bronstein, A., Bronstein, M., Bu, S., Castellani, U., Cheng, S., Garro, V., Giachetti, A., Godil, A., Isaia, L., Han, J., Johan, H., Lai, L., Li, B., Li, C., Li, H., Litman, R., Liu, X., Liu, Z., Lu, Y., Sun, L., Tam, G., Tatsuma, A. and Ye, J. 2016. Shape retrieval of non-rigid 3D human models. International Journal of Computer Vision 120 (2) , pp. 169-193. 10.1007/s11263-016-0903-8

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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. We extend our recent paper which provided a benchmark for testing non-rigid 3D shape retrieval algorithms on 3D human models. This benchmark provided a far stricter challenge than previous shape benchmarks. We have added 145 new models for use as a separate training set, in order to standardise the training data used and provide a fairer comparison. We have also included experiments with the FAUST dataset of human scans. All participants of the previous benchmark study have taken part in the new tests reported here, many providing updated results using the new data. In addition, further participants have also taken part, and we provide extra analysis of the retrieval results. A total of 25 different shape retrieval methods are compared.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Publisher: Springer Verlag
ISSN: 0920-5691
Funders: EPSRC
Date of First Compliant Deposit: 5 May 2016
Date of Acceptance: 4 April 2016
Last Modified: 11 May 2023 14:15

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