Back to Search
Start Over
Image-Driven Furniture Style for Interactive 3D Scene Modeling
- Publication Year :
- 2020
-
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.<br />Comment: Accepted to Pacific Graphics 2020
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2010.10557
- Document Type :
- Working Paper