ARQGAN: an evaluation of Generative Adversarial Networks’ approaches for automatic virtual restoration of Greek temples.

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Expert Systems with Applications
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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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Deep Learning, Generative Adversarial Networks, Image inpainting, Segmented training, Virtual restoration, Greek temples