Image-Driven Furniture Style for Interactive 3D Scene Modeling

• URL: http://arxiv.org/abs/2010.10557v1
• Date: Tue, 20 Oct 2020 18:19:28 GMT
• Title: Image-Driven Furniture Style for Interactive 3D Scene Modeling
• Authors: Tomer Weiss, Ilkay Yildiz, Nitin Agarwal, Esra Ataer-Cansizoglu, Jae-Woo Choi
• Abstract summary: Interior style follows rules involving color, geometry and other visual elements. We propose a method for fast-tracking style-similarity tasks, by learning a furniture's style-compatibility from interior scene images. We demonstrate our method with several 3D model style-compatibility results, and with an interactive system for modeling style-consistent scenes.
• Score: 8.8561720398658
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Creating realistic styled spaces is a complex task, which involves design know-how for what furniture pieces go well together. Interior style follows abstract rules involving color, geometry and other visual elements. Following such rules, users manually select similar-style items from large repositories of 3D furniture models, a process which is both laborious and time-consuming. We propose a method for fast-tracking style-similarity tasks, by learning a furniture's style-compatibility from interior scene images. Such images contain more style information than images depicting single furniture. To understand style, we train a deep learning network on a classification task. Based on image embeddings extracted from our network, we measure stylistic compatibility of furniture. We demonstrate our method with several 3D model style-compatibility results, and with an interactive system for modeling style-consistent scenes.

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