Hierarchical Reinforcement Learning for Furniture Layout in Virtual Indoor Scenes

TL;DR

This paper introduces a hierarchical reinforcement learning approach to automatically generate furniture layouts in virtual indoor scenes, improving layout quality over existing methods.

Contribution

The paper presents a novel HRL framework with curriculum learning for furniture layout in VR, validated on a large real-world dataset.

Findings

Higher-quality furniture layouts achieved

Outperforms state-of-the-art models

Effective in virtual reality environments

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.