论文标题
通过多域学习的定制大小家具的深层布局
Deep Layout of Custom-size Furniture through Multiple-domain Learning
论文作者
论文摘要
在本文中，我们提出了一个多域模型，用于在室内场景中生成定制大小的家具布局。该型号旨在支持专业的室内设计师，以更快地生产具有定制尺寸家具的室内装饰解决方案。提出的模型结合了深层布局模块，一个域注意模块，尺寸域传输模块以及端端训练中的自定义大小模块。与现场合成的先前工作相比，我们提出的模型增强了内部房间自动尺寸家具的能力。我们在现实世界中的内部布局数据集上进行实验，该数据集包含$ 710,700 $的设计。我们的数值结果表明，与最先进的模型相比，所提出的模型产生了定制大小的家具的更高质量的布局。
In this paper, we propose a multiple-domain model for producing a custom-size furniture layout in the interior scene. This model is aimed to support professional interior designers to produce interior decoration solutions with custom-size furniture more quickly. The proposed model combines a deep layout module, a domain attention module, a dimensional domain transfer module, and a custom-size module in the end-end training. Compared with the prior work on scene synthesis, our proposed model enhances the ability of auto-layout of custom-size furniture in the interior room. We conduct our experiments on a real-world interior layout dataset that contains $710,700$ designs from professional designers. Our numerical results demonstrate that the proposed model yields higher-quality layouts of custom-size furniture in comparison with the state-of-art model.
