Deep Layout of Custom-size Furniture through Multiple-domain Learning

TL;DR

This paper introduces a multi-domain deep learning model that enables automatic generation of custom-size furniture layouts in interior scenes, significantly aiding interior designers with faster and higher-quality solutions.

Contribution

The paper presents a novel multi-domain deep learning architecture that improves auto-layout of custom-size furniture in interior design, outperforming existing models.

Findings

Higher-quality furniture layouts achieved

Model outperforms state-of-the-art in accuracy

Supports professional interior design workflows

Abstract

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.