The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B2-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 415–420, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-415-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2020, 415–420, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B2-2020-415-2020

  12 Aug 2020

12 Aug 2020

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

V. Gorbatsevich, B. Kulgildin, M. Melnichenko, O. Vygolov, and Y. Vizilter V. Gorbatsevich et al.
  • Federal State Unitary Enterprise «State Research Institute of Aviation Systems», Russian Federation

Keywords: CNN, Cityscape 3D model, GAN, Monocular 3D Reconstruction, Heightmaps

Abstract. 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.