Paper Info
Title
Graph-based generative representation learning of semantically and behaviorally augmented floorplans
Abstract
Floorplans are commonly used to represent the layout of buildings. Research works toward computational techniques that facilitate the design process, such as automated analysis and optimization, often using simple floorplan representations that ignore the space’s semantics and do not consider usage-related analytics. We present a floorplan embedding technique that uses an attributed graph to model the floorplans’ geometric information, design semantics, and behavioral features as the node and edge attributes. A long short-term memory (LSTM) variational autoencoder (VAE) architecture is proposed and trained to embed attributed graphs as vectors in a continuous space. A user study is conducted to evaluate the coupling of similar floorplans retrieved from the embedding space for a given input (e.g., design layout). The qualitative, quantitative, and user study evaluations show that our embedding framework produces meaningful and accurate vector representations for floorplans. Besides, our proposed model is generative. We studied and showcased its effectiveness for generating new floorplans. We also release the dataset that we have constructed. We include the design semantic attributes and simulation-generated human behavioral features for each floorplan in the dataset for further study in the community.
Year
DOI
Venue
2022
10.1007/s00371-021-02155-w
The Visual Computer
Keywords
DocType
Volume
Floorplan representation, Floorplan generation, LSTM Variational autoencoder, Attributed graph, Design semantic features, Human behavioral features
Journal
38
Issue
ISSN
Citations 
8
0178-2789
0
PageRank 
References 
Authors
0.34
9
5
Name
Order
Citations
PageRank
Vahid Azizi100.34
Muhammad Usman231677.54
Honglu Zhou3163.64
Petros Faloutsos43082422.32
Mubbasir Kapadia554658.07