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.

中文翻译：

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用于交互式 3D 场景建模的图像驱动家具风格

创建逼真风格的空间是一项复杂的任务，其中涉及如何搭配家具的设计诀窍。室内风格遵循涉及颜色、几何和其他视觉元素的抽象规则。遵循这些规则，用户从大型 3D 家具模型库中手动选择相似风格的物品，这一过程既费力又费时。我们通过从室内场景图像中学习家具的风格兼容性，提出了一种快速跟踪风格相似性任务的方法。此类图像比描绘单个家具的图像包含更多的风格信息。为了理解风格，我们在分类任务上训练了一个深度学习网络。基于从我们的网络中提取的图像嵌入，我们测量家具的风格兼容性。