Floor plans are the basis of reasoning in and communicating about indoor environments. In this paper, we show that by modelling floor plans as sequences of line segments seen from a particular point of view, recent advances in autoregressive sequence modelling can be leveraged to model and predict floor plans. The line segments are canonicalized and translated to sequence of tokens and an attention-based neural network is used to fit a one-step distribution over next tokens. We fit the network to sequences derived from a set of large-scale floor plans, and demonstrate the capabilities of the model in four scenarios: novel floor plan generation, completion of partially observed floor plans, generation of floor plans from simulated sensor data, and finally, the applicability of a floor plan model in predicting the shortest distance with partial knowledge of the environment.
Visit the project website.
FloorGenT was developed on a Python 3.6 stack. We recommend using pyenv or
similar to install a Python 3.6.x interpreter, then create a virtualenv.
requirements.txt are the high-level package requirements, you can choose to
install this by ./env/bin/pip install -r ./requirements.txt and allow pip to
decide what subdependencies to install; or you can choose to use the file
./requirements-snapshot.txt which is a full snapshot of the versions we used.