Multi-Agent Reinforcement Learning of 3D Furniture Layout Simulation in Indoor Graphics Scenes

18 Feb 2021  ·  Xinhan Di, Pengqian Yu ·

In the industrial interior design process, professional designers plan the furniture layout to achieve a satisfactory 3D design for selling. In this paper, we explore the interior graphics scenes design task as a Markov decision process (MDP) in 3D simulation, which is solved by multi-agent reinforcement learning. The goal is to produce furniture layout in the 3D simulation of the indoor graphics scenes. In particular, we firstly transform the 3D interior graphic scenes into two 2D simulated scenes. We then design the simulated environment and apply two reinforcement learning agents to learn the optimal 3D layout for the MDP formulation in a cooperative way. 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{}.

PDF Abstract

Results from the Paper

  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.


No methods listed for this paper. Add relevant methods here