MSD: A Benchmark Dataset for Floor Plan Generation of Building Complexes

Casper van Engelenburg; Fatemeh Mostafavi; Emanuel Kuhn; Yuntae Jeon; Michael Franzen; Matthias Standfest; Jan van Gemert; Seyran Khademi

doi:10.1007/978-3-031-73636-0_4

MSD: A Benchmark Dataset for Floor Plan Generation of Building Complexes

Casper van Engelenburg^*, Fatemeh Mostafavi, Emanuel Kuhn, Yuntae Jeon, Michael Franzen, Matthias Standfest, Jan van Gemert, Seyran Khademi

^*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

Abstract

Diverse and realistic floor plan data are essential for the development of useful computer-aided methods in architectural design. Today’s large-scale floor plan datasets predominantly feature simple floor plan layouts, typically representing single-apartment dwellings only. To compensate for the mismatch between current datasets and the real world, we develop Modified Swiss Dwellings (MSD) – the first large-scale floor plan dataset that contains a significant share of layouts of multi-apartment dwellings. MSD features over 5.3K floor plans of medium- to large-scale building complexes, covering over 18.9K distinct apartments. We validate that existing approaches for floor plan generation, while effective in simpler scenarios, cannot yet seamlessly address the challenges posed by MSD. Our benchmark calls for new research in floor plan machine understanding. Code and data are open.

Original language	English
Title of host publication	Computer Vision – ECCV 2024
Subtitle of host publication	18th European Conference, Milan, Italy, September 29–October 4, 2024, Proceedings, Part LVIII
Editors	Aleš Leonardis, Elisa Ricci, Stefan Roth, Olga Russakovsky, Torsten Sattler, Gül Varol
Place of Publication	Cham
Publisher	Springer
Pages	60-75
Number of pages	16
ISBN (Electronic)	978-3-031-73636-0
ISBN (Print)	978-3-031-73635-3
DOIs	https://doi.org/10.1007/978-3-031-73636-0_4
Publication status	Published - 2025
Event	European Conference on Computer Vision – ECCV 2024 - MiCo Milano, Milan, Italy Duration: 29 Sept 2024 → 4 Oct 2024 https://eccv.ecva.net/

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	15116 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	European Conference on Computer Vision – ECCV 2024
Abbreviated title	ECCV 2024
Country/Territory	Italy
City	Milan
Period	29/09/24 → 4/10/24
Internet address	https://eccv.ecva.net/

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

Benchmark Dataset
Diffusion Models
Floor Plan Generation

Access to Document

10.1007/978-3-031-73636-0_4

Cite this

van Engelenburg, C., Mostafavi, F., Kuhn, E., Jeon, Y., Franzen, M., Standfest, M., van Gemert, J., & Khademi, S. (2025). MSD: A Benchmark Dataset for Floor Plan Generation of Building Complexes. In A. Leonardis, E. Ricci, S. Roth, O. Russakovsky, T. Sattler, & G. Varol (Eds.), Computer Vision – ECCV 2024: 18th European Conference, Milan, Italy, September 29–October 4, 2024, Proceedings, Part LVIII (pp. 60-75). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 15116 LNCS). Springer. https://doi.org/10.1007/978-3-031-73636-0_4

van Engelenburg, Casper ; Mostafavi, Fatemeh ; Kuhn, Emanuel et al. / MSD : A Benchmark Dataset for Floor Plan Generation of Building Complexes. Computer Vision – ECCV 2024: 18th European Conference, Milan, Italy, September 29–October 4, 2024, Proceedings, Part LVIII. editor / Aleš Leonardis ; Elisa Ricci ; Stefan Roth ; Olga Russakovsky ; Torsten Sattler ; Gül Varol. Cham : Springer, 2025. pp. 60-75 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "MSD: A Benchmark Dataset for Floor Plan Generation of Building Complexes",

abstract = "Diverse and realistic floor plan data are essential for the development of useful computer-aided methods in architectural design. Today{\textquoteright}s large-scale floor plan datasets predominantly feature simple floor plan layouts, typically representing single-apartment dwellings only. To compensate for the mismatch between current datasets and the real world, we develop Modified Swiss Dwellings (MSD) – the first large-scale floor plan dataset that contains a significant share of layouts of multi-apartment dwellings. MSD features over 5.3K floor plans of medium- to large-scale building complexes, covering over 18.9K distinct apartments. We validate that existing approaches for floor plan generation, while effective in simpler scenarios, cannot yet seamlessly address the challenges posed by MSD. Our benchmark calls for new research in floor plan machine understanding. Code and data are open.",

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author = "{van Engelenburg}, Casper and Fatemeh Mostafavi and Emanuel Kuhn and Yuntae Jeon and Michael Franzen and Matthias Standfest and {van Gemert}, Jan and Seyran Khademi",

note = "Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.; European Conference on Computer Vision – ECCV 2024, ECCV 2024 ; Conference date: 29-09-2024 Through 04-10-2024",

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van Engelenburg, C , Mostafavi, F, Kuhn, E, Jeon, Y, Franzen, M, Standfest, M, van Gemert, J & Khademi, S 2025, MSD: A Benchmark Dataset for Floor Plan Generation of Building Complexes. in A Leonardis, E Ricci, S Roth, O Russakovsky, T Sattler & G Varol (eds), Computer Vision – ECCV 2024: 18th European Conference, Milan, Italy, September 29–October 4, 2024, Proceedings, Part LVIII. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 15116 LNCS, Springer, Cham, pp. 60-75, European Conference on Computer Vision – ECCV 2024, Milan, Italy, 29/09/24. https://doi.org/10.1007/978-3-031-73636-0_4

MSD: A Benchmark Dataset for Floor Plan Generation of Building Complexes. / van Engelenburg, Casper ; Mostafavi, Fatemeh; Kuhn, Emanuel et al.
Computer Vision – ECCV 2024: 18th European Conference, Milan, Italy, September 29–October 4, 2024, Proceedings, Part LVIII. ed. / Aleš Leonardis; Elisa Ricci; Stefan Roth; Olga Russakovsky; Torsten Sattler; Gül Varol. Cham: Springer, 2025. p. 60-75 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 15116 LNCS).

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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AU - Mostafavi, Fatemeh

AU - Kuhn, Emanuel

AU - Jeon, Yuntae

AU - Franzen, Michael

AU - Standfest, Matthias

AU - van Gemert, Jan

AU - Khademi, Seyran

N1 - Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

PY - 2025

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N2 - Diverse and realistic floor plan data are essential for the development of useful computer-aided methods in architectural design. Today’s large-scale floor plan datasets predominantly feature simple floor plan layouts, typically representing single-apartment dwellings only. To compensate for the mismatch between current datasets and the real world, we develop Modified Swiss Dwellings (MSD) – the first large-scale floor plan dataset that contains a significant share of layouts of multi-apartment dwellings. MSD features over 5.3K floor plans of medium- to large-scale building complexes, covering over 18.9K distinct apartments. We validate that existing approaches for floor plan generation, while effective in simpler scenarios, cannot yet seamlessly address the challenges posed by MSD. Our benchmark calls for new research in floor plan machine understanding. Code and data are open.

AB - Diverse and realistic floor plan data are essential for the development of useful computer-aided methods in architectural design. Today’s large-scale floor plan datasets predominantly feature simple floor plan layouts, typically representing single-apartment dwellings only. To compensate for the mismatch between current datasets and the real world, we develop Modified Swiss Dwellings (MSD) – the first large-scale floor plan dataset that contains a significant share of layouts of multi-apartment dwellings. MSD features over 5.3K floor plans of medium- to large-scale building complexes, covering over 18.9K distinct apartments. We validate that existing approaches for floor plan generation, while effective in simpler scenarios, cannot yet seamlessly address the challenges posed by MSD. Our benchmark calls for new research in floor plan machine understanding. Code and data are open.

KW - Benchmark Dataset

KW - Diffusion Models

KW - Floor Plan Generation

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DO - 10.1007/978-3-031-73636-0_4

M3 - Conference contribution

AN - SCOPUS:85209205855

SN - 978-3-031-73635-3

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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PB - Springer

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van Engelenburg C , Mostafavi F, Kuhn E, Jeon Y, Franzen M, Standfest M et al. MSD: A Benchmark Dataset for Floor Plan Generation of Building Complexes. In Leonardis A, Ricci E, Roth S, Russakovsky O, Sattler T, Varol G, editors, Computer Vision – ECCV 2024: 18th European Conference, Milan, Italy, September 29–October 4, 2024, Proceedings, Part LVIII. Cham: Springer. 2025. p. 60-75. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-73636-0_4