SemLayoutDiff：基于扩散模型的语义布局生成用于室内场景合成
SemLayoutDiff: Semantic Layout Generation with Diffusion Model for Indoor Scene Synthesis
AI总结：
SemLayoutDiff提出一种结合语义图与分类扩散模型的统一框架，实现多房间类型下受建筑约束的多样化3D室内场景合成，并在3D-FRONT数据集上超越现有方法。
摘要：
我们提出了SemLayoutDiff，一个用于在多种房间类型中合成多样化3D室内场景的统一模型。该模型引入了一种场景布局表示，结合了俯视语义图和每个对象的属性。与先前无法适应建筑约束的方法不同，SemLayoutDiff采用了一种分类扩散模型，能够显式地根据房间掩码对场景合成进行条件化。它首先生成连贯的语义图，随后通过一个基于交叉注意力的网络来预测尊重合成布局的家具摆放位置。我们的方法还考虑了门和窗等建筑元素，确保生成的家具布置保持实用且不遮挡。在3D-FRONT数据集上的实验表明，SemLayoutDiff能够生成空间连贯、逼真且多样化的场景，性能优于先前的方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
