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

• URL: http://arxiv.org/abs/2012.08131v1
• Date: Tue, 15 Dec 2020 07:32:13 GMT
• Title: Deep Layout of Custom-size Furniture through Multiple-domain Learning
• Authors: Xinhan Di, Pengqian Yu, Danfeng Yang, Hong Zhu, Changyu Sun, YinDong Liu
• Abstract summary: 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. We conduct experiments on a real-world interior layout dataset that contains $710,700$ designs from professional designers.
• Score: 6.259404056725123
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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