Floor Plan Exploration Framework Based on Similarity Distances

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Date
2022
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
Computational methods to compute similarities between floor plans can help architects explore floor plans in large datasets to avoid duplication of designs and to search for existing plans that satisfy their needs. Recently, LayoutGMN [PLF*21] delivered state-of-the-art performance for computing similarity scores between floor plans. However, the high computational costs of LayoutGMN make it unsuitable for the aforementioned applications. In this paper, we significantly reduced the times needed to query results computed by LayoutGMN by projecting the floor plans into a common low-dimensional (e.g., three) data space. The projection is done by optimizing for coordinates of floor plans with Euclidean distances mimicking their similarity scores originally calculated by LayoutGMN. Quantitative and qualitative evaluations show that our results match the distributions of the original LayoutGMN similarity scores. User study shows that our similarity results largely match human expectations.
Description

CCS Concepts: Computing methodologies -> Modeling methodologies

        
@inproceedings{
10.2312:stag.20221263
, booktitle = {
Smart Tools and Applications in Graphics - Eurographics Italian Chapter Conference
}, editor = {
Cabiddu, Daniela
and
Schneider, Teseo
and
Allegra, Dario
and
Catalano, Chiara Eva
and
Cherchi, Gianmarco
and
Scateni, Riccardo
}, title = {{
Floor Plan Exploration Framework Based on Similarity Distances
}}, author = {
Shih, Chia-Ying
and
Peng, Chi-Han
}, year = {
2022
}, publisher = {
The Eurographics Association
}, ISSN = {
2617-4855
}, ISBN = {
978-3-03868-191-5
}, DOI = {
10.2312/stag.20221263
} }
Citation