http://www.cnr.it/ontology/cnr/individuo/prodotto/ID280241

SHREC'14 Track: Retrieval and Classification on Textured 3D Models (Contributo in atti di convegno)

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SHREC'14 Track: Retrieval and Classification on Textured 3D Models (Contributo in atti di convegno) (literal)

Anno

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi

Alternative label

S. Biasotti, A. Cerri, M. Abdelrahman, M. Aono, A. Ben Hamza, M. El-Melegy, A. Farag, V. Garro, A. Giachetti, D. Giorgi, A. Godil, C. Li, Y.-J. Liu, H. Y. Martono, C. Sanada, A. Tatsuma, S. Velasco-Forero, C.-X. Xu (2014)
SHREC'14 Track: Retrieval and Classification on Textured 3D Models
in Eurographics Workshop on 3D Object Retrieval (2014), Strasbourg, France, April 6, 2014
(literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori

S. Biasotti, A. Cerri, M. Abdelrahman, M. Aono, A. Ben Hamza, M. El-Melegy, A. Farag, V. Garro, A. Giachetti, D. Giorgi, A. Godil, C. Li, Y.-J. Liu, H. Y. Martono, C. Sanada, A. Tatsuma, S. Velasco-Forero, C.-X. Xu (literal)

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Istituto di Matematica Applicata e Tecnologie Informatiche \"E. Magenes\", CNR, Italy Computer Vision and Image Processing Laboratory (CVIP Lab), University of Louisville, KY, USA Department of Computer Science and Engineering, Toyohashi University of Technology, Japan Concordia University, Canada Dipartimento di Informatica, Università di Verona, Italy Istituto di Scienza e Tecnologie dell'Informazione \"A. Faedo\", CNR, Italy National Institute of Standards and Technology, USA Department of Computer Science and Technology, Tsinghua University, P. R. China Department of Mathematics, National University of Singapore, Singapore (literal)

Titolo

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Abstract

This paper reports the results of the SHREC'14 track: Retrieval and classification on textured 3D models, whose goal is to evaluate the performance of retrieval algorithms when models vary either by geometric shape or texture, or both. The collection to search in is made of 572 textured mesh models, having a two-level classification based on geometry and texture. Together with the dataset, a training set of 96 models was provided. The track saw eight participants and the submission of 22 runs, to either the retrieval or the classification contest, or both. The evaluation results show a promising scenario about textured 3D retrieval methods, and reveal interesting insights in dealing with texture information in the CIELab rather than in the RGB colour space. (literal)

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