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Authors: Simon Hensel 1 ; Steffen Goebbels 1 and Martin Kada 2

Affiliations: 1 Institute for Pattern Recognition, Niederrhein University of Applied Sciences, Reinarzstrasse 49, Krefeld, Germany ; 2 Institute of Geodesy and Geoinformation Science, Technical University of Berlin, Kaiserin-Augusta-Allee 104-106, Berlin, Germany

ISBN: 978-989-758-488-6

ISSN: 2184-4321

Keyword(s): Deep Learning, LSTM, Facade Reconstruction, Structure Completion.

Abstract: 3D city models are often generated from oblique aerial images and photogrammetric point clouds. In contrast to roof surfaces, facades can not directly be reconstructed in a similar high level of quality from this data. Distortions of perspective might appear in images, due to the camera angle. Occlusions and shadowing occur as well. Objects, such as windows and doors, will have to be detected on such data if facades are to be reconstructed. Although one can use inpainting techniques to cover occluded areas, detection results are often incomplete and noisy. Formal grammars can then be used to align and add objects. However, it is difficult to find suitable rules for all types of buildings. We propose a post-processing approach based on neural networks to improve facade layouts. To this end, we applied existing Recurrent Neural Network architectures like Multi-Dimensional Long Short-term Memory Network and Quasi Recurrent Neural Network in a new context. We also propose a novel architec ture, the Rotated Multi-Dimensional Long Short Term Memory. In order to deal with two-dimensional neighborhoods this architecture combines four two-dimensional Multi-Dimensional Long Short-term Memory Networks on rotated images. We could improve the quality of detection results on the Graz50 data set. (More)

CC BY-NC-ND 4.0

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Paper citation in several formats:
Hensel, S.; Goebbels, S. and Kada, M. (2021). LSTM Architectures for Facade Structure Completion. In Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: GRAPP, ISBN 978-989-758-488-6 ISSN 2184-4321, pages 15-24. DOI: 10.5220/0010194400150024

@conference{grapp21,
author={Simon Hensel. and Steffen Goebbels. and Martin Kada.},
title={LSTM Architectures for Facade Structure Completion},
booktitle={Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: GRAPP,},
year={2021},
pages={15-24},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010194400150024},
isbn={978-989-758-488-6},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: GRAPP,
TI - LSTM Architectures for Facade Structure Completion
SN - 978-989-758-488-6
IS - 2184-4321
AU - Hensel, S.
AU - Goebbels, S.
AU - Kada, M.
PY - 2021
SP - 15
EP - 24
DO - 10.5220/0010194400150024

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