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

Casper van Engelenburg, Fatemeh Mostafavi, Emanuel Kuhn, Y. Jeon, Michael Franzen +3 more
2/14/2026

No confident code implementation found for this paper. 2 possible matches can be shown below.

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 \textbf{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.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 2 weaker matches are hidden by default — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Cite this paper

@article{engelenburg2026msd,
  title  = {MSD: A Benchmark Dataset for Floor Plan Generation of Building Complexes},
  author = {Casper van Engelenburg and Fatemeh Mostafavi and Emanuel Kuhn and Y. Jeon and Michael Franzen and Matthias Standfest and Jan van Gemert and Seyran Khademi},
  year   = {2026},
  eprint = {2407.10121},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/2407.10121},
  journal = {ECCV 2024 2024}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2407.10121.svg)](https://codeofpaper.com/paper/2407.10121)

Discussion