Graph-Based Generative Representation Learning of Semantically and Behaviorally Augmented Floorplans
This work provides a method for architects and designers to better analyze and generate floorplans by incorporating semantic and behavioral data, addressing limitations of simpler representations.
This paper proposes a floorplan embedding technique that represents geometric information, design semantics, and behavioral features as an attributed graph. It uses an LSTM Variational Autoencoder to embed these graphs into a continuous vector space, enabling the retrieval of similar floorplans and the generation of new ones.
Floorplans are commonly used to represent the layout of buildings. In computer aided-design (CAD) floorplans are usually represented in the form of hierarchical graph structures. Research works towards computational techniques that facilitate the design process, such as automated analysis and optimization, often use simple floorplan representations that ignore the semantics of the space and do not take into account usage related analytics. We present a floorplan embedding technique that uses an attributed graph to represent the geometric information as well as design semantics and behavioral features of the inhabitants as 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 with respect to 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. In addition, our proposed model is a generative model. We studied and showcased its effectiveness for generating new floorplans. We also release the dataset that we have constructed and which, for each floorplan, includes the design semantics attributes as well as simulation generated human behavioral features for further study in the community.