Hierarchical Reinforcement Learning for Furniture Layout in Virtual Indoor Scenes

• URL: http://arxiv.org/abs/2210.10431v1
• Date: Wed, 19 Oct 2022 09:58:10 GMT
• Title: Hierarchical Reinforcement Learning for Furniture Layout in Virtual Indoor Scenes
• Authors: Xinhan Di and Pengqian Yu
• Abstract summary: In this paper, we explore the furniture layout task as a Markov decision process (MDP) in virtual reality. The goal is to produce a proper two-furniture layout in the virtual reality of the indoor scenes.
• Score: 2.2481284426718533
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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