In real life, the decoration of 3D indoor scenes through designing furniture layout provides a rich experience for people. In this paper, we explore the furniture layout task as a Markov decision process (MDP) in virtual reality, which is solved by hierarchical reinforcement learning (HRL). The goal is to produce a proper two-furniture layout in the virtual reality of the indoor scenes. In particular, we first design a simulation environment and introduce the HRL formulation for a two-furniture layout. We then apply a hierarchical actor-critic algorithm with curriculum learning to solve the MDP. 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 models.
@article{arxiv.2210.10431,
title = {Hierarchical Reinforcement Learning for Furniture Layout in Virtual Indoor Scenes},
author = {Xinhan Di and Pengqian Yu},
journal= {arXiv preprint arXiv:2210.10431},
year = {2022}
}
Comments
Accepted by Reinforcement Learning for Real Life Workshop @ NeurIPS 2022