Structural Plan of Indoor Scenes with Personalized Preferences

TL;DR

This paper presents a model that automatically generates personalized indoor scene layouts, aiding interior designers by aligning with property owners' preferences, and demonstrates its effectiveness on a large real-world dataset.

Contribution

The paper introduces a novel assistive model for personalized indoor scene layout generation, utilizing abstract graph extraction and conditional scene instantiation, supported by a new extensive interior design dataset.

Findings

The model effectively generates layouts matching user preferences.

Numerical results outperform state-of-the-art methods.

The dataset contains 11,000 real-world interior designs.

Abstract

In this paper, we propose an assistive model that supports professional interior designers to produce industrial interior decoration solutions and to meet the personalized preferences of the property owners. The proposed model is able to automatically produce the layout of objects of a particular indoor scene according to property owners' preferences. In particular, the model consists of the extraction of abstract graph, conditional graph generation, and conditional scene instantiation. We provide an interior layout dataset that contains real-world 11000 designs from professional designers. Our numerical results on the dataset demonstrate the effectiveness of the proposed model compared with the state-of-art methods.