SHREC 2020: Retrieval of digital surfaces with similar geometric reliefs
Author
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Moscoso Thompson, Elia
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Biasotti, Silvia
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Giachetti, Andrea
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Tortorici, Claudio
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Werghi, Naoufel
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Obeid, Ahmad Shaker
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Berretti, Stefano
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Nguyen-Dinh, H. P.
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Minh-Qua, Le
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Hai-Dang, Nguyen
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Minh, Triet Tran
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Gigli, Leonardo
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Velasco Forero, Santiago
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Marcotegui, Beatriz
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Sipiran, Iván
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Bustos, Benjamín
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Romanelis, Ioannis
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Fotis, Vlassis
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Arvanitis, Gerasimos
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Moustakas, Konstantinos
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Otu, Ekpo
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Zwiggelaar, Reyer
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Hunter, David
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Liu, Yonghuai
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Arteaga, Yoko
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Luxman, Ramamoorthy
Admission date
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2021-01-27T15:42:42Z
Available date
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2021-01-27T15:42:42Z
Publication date
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2020
Cita de ítem
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Computers & Graphics 91 (2020) 199–218
es_ES
Identifier
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10.1016/j.cag.2020.07.011
Identifier
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https://repositorio.uchile.cl/handle/2250/178356
Abstract
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This paper presents the methods that have participated in the SHREC’20 contest on retrieval of surface patches with similar geometric reliefs and the analysis of their performance over the benchmark created for this challenge. The goal of the context is to verify the possibility of retrieving 3D models only based on the reliefs that are present on their surface and to compare methods that are suitable for this task. This problem is related to many real world applications, such as the classification of cultural heritage goods or the analysis of different materials. To address this challenge, it is necessary to characterize the local ”geometric pattern”information, possibly forgetting model size and bending. Seven groups participated in this contest and twenty runs were submitted for evaluation. The performances of the methods reveal that good results are achieved with a number of techniques that use different approaches.
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Patrocinador
dc.description.sponsorship
CNR-IMATI
DIT.AD0 04.100
DIT.AD021.080.001
Vingroup Innovation Foundation (VINIF)
VINIF.2019.DA19
Horizon 2020 programme of the European Union
813789
Proyecto de Mejoramiento y Ampliacin de los Servicios del Sistema Nacional de Ciencia Tecnolog.a e Innovacin Tecnolgica(Banco Mundial, Concytec)
062-2018-FONDECYT-BM-IADT-AV
Millennium Institute Foundational Research on Data (IMFD)