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Graph-based generative representation learning of semantically and behaviorally augmented floorplans

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Abstract

Floorplans are commonly used to represent the layout of buildings. Research works toward computational techniques that facilitate the design process, such as automated analysis and optimization, often using simple floorplan representations that ignore the space’s semantics and do not consider usage-related analytics. We present a floorplan embedding technique that uses an attributed graph to model the floorplans’ geometric information, design semantics, and behavioral features as the node and edge attributes. A long short-term memory (LSTM) variational autoencoder (VAE) architecture is proposed and trained to embed attributed graphs as vectors in a continuous space. A user study is conducted to evaluate the coupling of similar floorplans retrieved from the embedding space for a given input (e.g., design layout). The qualitative, quantitative, and user study evaluations show that our embedding framework produces meaningful and accurate vector representations for floorplans. Besides, our proposed model is generative. We studied and showcased its effectiveness for generating new floorplans. We also release the dataset that we have constructed. We include the design semantic attributes and simulation-generated human behavioral features for each floorplan in the dataset for further study in the community.

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Acknowledgements

This research has been partially funded by grants from ISSUM, Ontario Graduate Scholarship, and in part by NSF awards: IIS-1703883, IIS-1955404, and IIS-1955365. The authors would also like to thank Mathew Schwartz for helping in editing and proofreading the manuscript.

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Authors and Affiliations

  1. Computer Science Department, Rutgers University, Piscataway, NJ, USA

    Vahid Azizi, Honglu Zhou & Mubbasir Kapadia

  2. Electrical Engineering and Computer Science Department, York University, Toronto, Canada

    Muhammad Usman & Petros Faloutsos

  3. UHN Toronto Rehabilitation Institute, Toronto, Canada

    Petros Faloutsos

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  1. Vahid Azizi
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  2. Muhammad Usman
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  4. Petros Faloutsos
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  5. Mubbasir Kapadia
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Correspondence to Vahid Azizi.

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Azizi, V., Usman, M., Zhou, H. et al. Graph-based generative representation learning of semantically and behaviorally augmented floorplans. Vis Comput 38, 2785–2800 (2022). https://doi.org/10.1007/s00371-021-02155-w

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