SEMI-AUTOMATIC CITYSCAPE 3D MODEL RESTORATION USING GENERATIVE ADVERSARIAL NETWORK

Gorbatsevich, V.; Kulgildin, B.; Melnichenko, M.; Vygolov, O.; Vizilter, Y.

The paper addresses the problem of a city heightmap restoration using satellite view image and some manually created area with 3D data. We propose the approach based on generative adversarial networks. Our algorithm contains three steps: low quality 3D restoration, buildings segmentation using restored model, and high-quality 3D restoration. CNN architecture based on original ResDilation blocks and ResNet is used for steps one and three. Training and test datasets were retrieved from National Lidar Dataset (United States) and the algorithm achieved approximately MSE = 3.84 m on this data. In addition, we tested our model on the completely different ISPRS Potsdam dataset and obtained MSE = 5.1 m.

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Gorbatsevich, V. / Kulgildin, B. / Melnichenko, M. / et al: SEMI-AUTOMATIC CITYSCAPE 3D MODEL RESTORATION USING GENERATIVE ADVERSARIAL NETWORK. 2020. Copernicus Publications.

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