Traditional indoor scene synthesis methods often take a two-step approach: object selection and object arrangement. Current state-of-the-art object selection approaches are based on convolutional neural networks (CNNs) and can produce realistic scenes for a single room. However, they cannot be directly extended to synthesize style-compatible scenes for multiple rooms with different functions. To address this issue, we treat the object selection problem as combinatorial optimization based on a Labeled LDA (L-LDA) model. We first calculate occurrence probability distribution of object categories according to a topic model, and then sample objects from each category considering their function diversity along with style compatibility, while regarding not only separate rooms, but also associations among rooms. User study shows that our method outperforms the baselines by incorporating multi-function and multi-room settings with style constraints, and sometimes even produces plausible scenes comparable to those produced by professional designers.

中文翻译：

---

多房间室内场景合成的风格兼容对象推荐

传统的室内场景合成方法通常采取两步方法：对象选择和对象排列。当前最先进的对象选择方法基于卷积神经网络 (CNN)，可以为单个房间生成逼真的场景。但是，它们不能直接扩展到为多个不同功能的房间合成风格兼容的场景。为了解决这个问题，我们将对象选择问题视为基于标记 LDA (L-LDA) 模型的组合优化。我们首先根据主题模型计算对象类别的发生概率分布，然后从每个类别中采样对象，考虑它们的功能多样性和风格兼容性，同时不仅考虑单独的房间，还考虑房间之间的关联。