Deep Reinforcement Learning for Producing Furniture Layout in Indoor Scenes
This addresses the need for efficient interior design automation in industrial settings, though it is an incremental improvement over existing methods.
The paper tackles the problem of automatically generating furniture layouts for indoor scenes by formulating it as a Markov decision process and solving it with deep reinforcement learning, achieving higher-quality layouts compared to the state-of-the-art model.
In the industrial interior design process, professional designers plan the size and position of furniture in a room to achieve a satisfactory design for selling. In this paper, we explore the interior scene design task as a Markov decision process (MDP), which is solved by deep reinforcement learning. The goal is to produce an accurate position and size of the furniture simultaneously for the indoor layout task. In particular, we first formulate the furniture layout task as a MDP problem by defining the state, action, and reward function. We then design the simulated environment and train reinforcement learning agents to produce the optimal layout for the MDP formulation. We conduct our experiments on a large-scale real-world interior layout dataset that contains industrial designs from professional designers. Our numerical results demonstrate that the proposed model yields higher-quality layouts as compared with the state-of-art model. The developed simulator and codes are available at \url{https://github.com/CODE-SUBMIT/simulator1}.