论文标题
在虚拟室内场景中的家具布局的分层增强学习
Hierarchical Reinforcement Learning for Furniture Layout in Virtual Indoor Scenes
论文作者
论文摘要
在现实生活中，通过设计家具布局来装饰3D室内场景为人们提供了丰富的体验。在本文中，我们在虚拟现实中探索了家具布局任务，作为马尔可夫决策过程（MDP），这是通过层次增强学习（HRL）解决的。目的是在室内场景的虚拟现实中生成适当的两轮式布局。特别是，我们首先设计了一个模拟环境，并引入了HRL配方，以实现两种功能的布局。然后，我们将使用课程学习的分层参与者批评算法来解决MDP。我们对包含专业设计师的工业设计的大型现实世界布局数据集进行了实验。我们的数值结果表明，与最先进的模型相比，所提出的模型产生更高质量的布局。
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.
