DiffuScene：用于生成式室内场景合成的去噪扩散模型
DiffuScene: Denoising Diffusion Models for Generative Indoor Scene Synthesis
AI总结：
本研究提出DiffuScene扩散模型，通过无序对象属性去噪生成3D室内场景，支持场景补全等多种应用，在3D-FRONT数据集上表现优于现有最优方法。
摘要：
我们提出了用于室内3D场景合成的DiffuScene，它基于一种新颖的场景配置去噪扩散模型。该模型生成存储在无序对象集中的3D实例属性，并为每个对象配置检索最相似的几何形状，对象配置由位置、尺寸、朝向、语义和几何特征等不同属性拼接而成。我们引入了一种扩散网络，通过对一组无序对象属性进行去噪来合成3D室内对象集合。无序参数化简化并促进了联合分布的近似。形状特征扩散有助于实现自然的对象摆放，包括对称摆放。我们的方法支持多种下游应用，包括场景补全、场景布局和文本条件场景合成。在3D-FRONT数据集上的实验表明，与现有最优方法相比，我们的方法能够合成更具物理合理性且更多样化的室内场景。大量消融实验验证了我们在场景扩散模型中所做设计选择的有效性。
英文摘要：
We present DiffuScene for indoor 3D scene synthesis based on a novel scene configuration denoising diffusion model. It generates 3D instance properties stored in an unordered object set and retrieves the most similar geometry for each object configuration, which is characterized as a concatenation of different attributes, including location, size, orientation, semantics, and geometry features. We introduce a diffusion network to synthesize a collection of 3D indoor objects by denoising a set of unordered object attributes. Unordered parametrization simplifies and eases the joint distribution approximation. The shape feature diffusion facilitates natural object placements, including symmetries. Our method enables many downstream applications, including scene completion, scene arrangement, and text-conditioned scene synthesis. Experiments on the 3D-FRONT dataset show that our method can synthesize more physically plausible and diverse indoor scenes than state-of-the-art methods. Extensive ablation studies verify the effectiveness of our design choice in scene diffusion models.
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