  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 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.
