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Enhancing detail of 3D terrain models using GAN
Author(s): Vladimir Gorbatsevich; Mikhail Melnichenko; Oleg Vygolov
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Paper Abstract

The paper addresses the problem of low quality 3D terrain models enhancement. We propose the approach based on convolutional neural networks (CNN), namely, on Pix2Pix method that uses generative adversarial networks for imageto-image translation. We use heightmap 3D terrain models representation to use classical CNNs. The network was trained on a synthetic dataset that included 150000 images and heightmaps of different landscapes. Our model showed the relative mean absolute difference equal to 0.459% on synthetic testing dataset. In addition, we demonstrate landscapes generation on the real data from Google Maps using our model.

Paper Details

Date Published: 21 June 2019
PDF: 7 pages
Proc. SPIE 11057, Modeling Aspects in Optical Metrology VII, 110571D (21 June 2019); doi: 10.1117/12.2525177
Show Author Affiliations
Vladimir Gorbatsevich, GosNIIAS (Russian Federation)
Mikhail Melnichenko, GosNIIAS (Russian Federation)
Oleg Vygolov, GosNIIAS (Russian Federation)


Published in SPIE Proceedings Vol. 11057:
Modeling Aspects in Optical Metrology VII
Bernd Bodermann; Karsten Frenner; Richard M. Silver, Editor(s)

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