Image-Driven Furniture Style for Interactive 3D Scene Modeling

TL;DR

This paper introduces a deep learning-based method that leverages interior scene images to assess furniture style compatibility, enabling faster and more intuitive 3D scene modeling.

Contribution

It presents a novel approach that learns furniture style-compatibility from interior images, improving efficiency in style-consistent scene creation.

Findings

Effective style-compatibility measurement from scene images

Enhanced speed in furniture selection process

Interactive system for style-aware scene modeling

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.