ARQGAN: an evaluation of Generative Adversarial Networks’ approaches for automatic virtual restoration of Greek temples.
Author: Nogales Moyano, Alberto; Delgado Martos, Emilio; Melchor, Ángel; García Tejedor, Álvaro José
Abstract: In 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.
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