SSIG: A Visually-Guided Graph Edit Distance for Floor Plan Similarity

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We propose a simple yet effective metric that measures structural similarity between visual instances of architectural floor plans, without the need for learning. Qualitatively, our experiments show that the retrieval results are similar to deeply learned methods. Effectively comparing instances of floor plan data is paramount to the success of machine understanding of floor plan data, including the assessment of floor plan generative models and floor plan recommendation systems. Comparing visual floor plan images goes beyond a sole pixel-wise visual examination and is crucially about similarities and differences in the shapes and relations between subdivisions that compose the layout. Currently, deep metric learning approaches are used to learn a pair-wise vector representation space that closely mimics the structural similarity, in which the models are trained on similarity labels that are obtained by Intersection-over-Union (IoU). To compensate for the lack of structural awareness in IoU, graph-based approaches such as Graph Matching Networks (GMNs) are used, which require pairwise inference for comparing data instances, making GMNs less practical for retrieval applications. In this paper, an effective evaluation metric for judging the structural similarity of floor plans, coined SSIG (Structural Similarity by IoU and GED), is proposed based on both image and graph distances. In addition, an efficient algorithm is developed that uses SSIG to rank a large-scale floor plan database. Code will be openly available.
Original languageEnglish
Title of host publicationProceedings - 2023 IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2023
Number of pages10
ISBN (Electronic)979-8-3503-0744-3
Publication statusPublished - 2023
EventICCV 2023: International Conference on Computer Vision - Paris, France
Duration: 2 Oct 20236 Oct 2023

Publication series

NameProceedings - 2023 IEEE/CVF International Conference on Computer Vision Workshops, ICCVW 2023


ConferenceICCV 2023: International Conference on Computer Vision

Bibliographical note

The version of this article that was uploaded to the research portal is an open access version of the IEEE Computer Society version that was uploaded in December 2023. There is a slight discrepancy between the page numbers of the definitive version (pp. 1565-1574) and the open access version (pp. 1573-1582).


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