Graph Similarity Learning of Floor Plans


Casper van Engelenburg (Delft University of Technology), Jan van Gemert (Delft University of Technology), Seyran Khademi (Delft University of Technology)
The 35th British Machine Vision Conference
PDFPosterSupplementary

Abstract

Floor plans depict building layouts and are often represented as graphs to capture the underlying spatial relationships. Comparison of these graphs is critical for applications like search, clustering, and data visualization. The most successful methods to compare graphs \ie, graph matching networks, rely on costly intermediate cross-graph node-level interactions, therefore being slow in inference time. We introduce \textbf{LayoutGKN}, a more efficient approach that postpones the cross-graph node-level interactions to the end of the joint embedding architecture. We do so by using a differentiable graph kernel as a distance function on the final learned node-level embeddings. We show that LayoutGKN computes similarity comparably or better than graph matching networks while significantly increasing the speed. Code: https://github.com/caspervanengelenburg/LayoutGKN.

Citation

@inproceedings{Engelenburg_2025_BMVC,
author    = {Casper van Engelenburg and Jan van Gemert and Seyran Khademi},
title     = {Graph Similarity Learning of Floor Plans},
booktitle = {36th British Machine Vision Conference 2025, {BMVC} 2025, Sheffield, UK, November 24-27, 2025},
publisher = {BMVA},
year      = {2025},
url       = {https://bmva-archive.org.uk/bmvc/2025/assets/papers/Paper_184/paper.pdf}
}


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