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Authors: Johannes Bayer ; Syed Saqib Bukhari and Andreas Dengel

Affiliation: German Research Center for Artificial Intelligence, Germany

ISBN: 978-989-758-276-9

Keyword(s): Archistant, Archistant WebUI, LSTM, Early Design Phases, Architectural Support.

Related Ontology Subjects/Areas/Topics: Applications ; Data Engineering ; Graphical and Graph-Based Models ; Information Retrieval ; Ontologies and the Semantic Web ; Pattern Recognition ; Shape Representation ; Software Engineering ; Theory and Methods ; Web Applications

Abstract: While computerized tools for late design phases are well-established in the architectural domain, early design phases still lack widespread, automated solutions. During these phases, the actual concept of a building is developed in a creative process which is conducted manually nowadays. In this paper, we present a novel strategy that tackles the problem in a semi-automated way, where long short-term memories (LSTMs) are making suggestions for each design step based on the user’s existing concept. A design step could be for example the creation of connections between rooms given a list of rooms or the creation of room layouts given a graph of connected rooms. This results in a tightly interleaved interaction between the user and the LSTMs. We propose two approaches for creating LSTMs with this behavior. In the first approach, one LSTM is trained for each design step. In the other approach, suggestions for all design steps are made by a single LSTM. We evaluate these approaches against each other by testing their performance on a set of floor plans. Finally, we present the integration of the best performing approach in an existing sketching software, resulting in an auto-completion for floor plans, similar to text auto-completion in modern office software. (More)

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Paper citation in several formats:
Bayer, J.; Bukhari, S. and Dengel, A. (2018). Interactive LSTM-Based Design Support in a Sketching Tool for the Architectural Domain - Floor Plan Generation and Auto Completion based on Recurrent Neural Networks.In Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-276-9, pages 115-123. DOI: 10.5220/0006589101150123

@conference{icpram18,
author={Johannes Bayer. and Syed Saqib Bukhari. and Andreas Dengel.},
title={Interactive LSTM-Based Design Support in a Sketching Tool for the Architectural Domain - Floor Plan Generation and Auto Completion based on Recurrent Neural Networks},
booktitle={Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2018},
pages={115-123},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006589101150123},
isbn={978-989-758-276-9},
}

TY - CONF

JO - Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Interactive LSTM-Based Design Support in a Sketching Tool for the Architectural Domain - Floor Plan Generation and Auto Completion based on Recurrent Neural Networks
SN - 978-989-758-276-9
AU - Bayer, J.
AU - Bukhari, S.
AU - Dengel, A.
PY - 2018
SP - 115
EP - 123
DO - 10.5220/0006589101150123

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