FloorGenT: Generative Vector Graphic Model of Floor Plans for Robotics

TL;DR

FloorGenT introduces a novel autoregressive model that generates, completes, and predicts indoor floor plans from partial data, aiding robotics and indoor environment reasoning.

Contribution

It models floor plans as sequences of line segments and applies sequence modeling techniques for diverse indoor environment tasks.

Findings

Successfully generated new floor plans.

Effectively completed partial floor plans.

Predicted shortest distances with partial environment data.

Abstract

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.