  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.
