用扩散模型生成多样且符合建筑约束的室内场景布局
SemLayoutDiff: Semantic Layout Generation with Diffusion Model for Indoor Scene Synthesis
- 结合语义地图与物体属性，用扩散模型生成布局
- 在3D-FRONT数据集上生成效果更真实、空间更连贯
- 支持门窗等结构约束，适合需要合理家具摆放的应用
我们提出SemLayoutDiff，一个统一模型，用于合成多种房间类型的多样化3D室内场景。该模型采用自上而下的语义地图与物体属性相结合的场景布局表示方式。与以往方法不同，该模型能显式地基于房间掩码进行条件控制，使用类别扩散模型生成连贯的语义地图，并通过基于交叉注意力的网络预测符合布局的家具位置。模型还考虑门、窗等建筑元素，确保家具摆放合理且不遮挡。在3D-FRONT数据集上的实验表明，SemLayoutDiff生成的场景在空间一致性、真实性和多样性方面均优于先前方法。
原文摘要 · Abstract (English)
We present SemLayoutDiff, a unified model for synthesizing diverse 3D indoor scenes across multiple room types. The model introduces a scene layout representation combining a top-down semantic map and attributes for each object. Unlike prior approaches, which cannot condition on architectural constraints, SemLayoutDiff employs a categorical diffusion model capable of conditioning scene synthesis explicitly on room masks. It first generates a coherent semantic map, followed by a cross-attention-based network to predict furniture placements that respect the synthesized layout. Our method also accounts for architectural elements such as doors and windows, ensuring that generated furniture arrangements remain practical and unobstructed. Experiments on the 3D-FRONT dataset show that SemLayoutDiff produces spatially coherent, realistic, and varied scenes, outperforming previous methods.
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