Image-Driven Furniture Style for Interactive 3D Scene Modeling

Document Type

Article

Publication Date

10-1-2020

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.

Identifier

85096418465 (Scopus)

Publication Title

Computer Graphics Forum

External Full Text Location

https://doi.org/10.1111/cgf.14126

e-ISSN

14678659

ISSN

01677055

First Page

57

Last Page

68

Issue

7

Volume

39

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