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dc.contributor.authorNogales Moyano, Alberto 
dc.contributor.authorDelgado Martos, Emilio
dc.contributor.authorMelchor, Ángel
dc.contributor.authorGarcía Tejedor, Álvaro José 
dc.date.accessioned2021-06-17T11:43:30Z
dc.date.available2021-06-17T11:43:30Z
dc.date.issued2021
dc.identifier.issn0957-4174spa
dc.identifier.urihttp://hdl.handle.net/10641/2335
dc.description.abstractIn the last years, Graphics Processing Units are evolving fast. This has had a big impact in several fields, such as Computer-Aided Design and particularly in 3D modeling, allowing the development of software for the creation of more detailed models. Nevertheless, building a 3D model is still a cumbersome and time-consuming task. Another field, that is evolving successfully due to this increase in computational capacity is Artificial Intelligence. These techniques are characterized among other things by the fact that they can automate tasks performed by humans. For example, reconstructing parts of images is being a hot topic recently. In this paper, a method based on Artificial Intelligence and in particular Deep Learning techniques is proposed to achieve this task. The aim is to automatically restore Greek temples based on renders of its ruins obtained from 3D model representations. Results show that adding segmented images to the training dataset gives better results. Also, restoration of the general part of the temples is well performed but the detailed elements have room for improvement.spa
dc.language.isoengspa
dc.publisherExpert Systems with Applicationsspa
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subjectDeep Learningspa
dc.subjectGenerative Adversarial Networksspa
dc.subjectImage inpaintingspa
dc.subjectSegmented trainingspa
dc.subjectVirtual restorationspa
dc.subjectGreek templesspa
dc.titleARQGAN: an evaluation of Generative Adversarial Networks’ approaches for automatic virtual restoration of Greek temples.spa
dc.typearticlespa
dc.description.versionpre-printspa
dc.rights.accessRightsopenAccessspa
dc.description.extent1236 KBspa
dc.identifier.doi10.1016/j.eswa.2021.115092spa
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/abs/pii/S0957417421005339spa


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