M3DLayout：一个多源的3D室内布局及结构描述数据集用于3D生成
M3DLayout: A Multi-Source Dataset of 3D Indoor Layouts and Structured Descriptions for 3D Generation
AI总结：
本文提出M3DLayout数据集，包含21367个布局和433k个物体实例，整合真实扫描、专业CAD设计和程序生成场景，为3D生成模型提供多样化的空间和语义学习基础。
摘要：
在文本驱动的3D场景生成中，物体布局作为连接高层语言指令与详细几何输出的关键中间表示。它不仅提供了确保物理合理性的结构蓝图，还支持语义可控性和交互编辑。然而，当前3D室内布局生成模型的学习能力受限于现有数据集的规模、多样性和标注质量。为此，我们引入M3DLayout，一个大规模、多源的数据集用于3D室内布局生成。M3DLayout包含21,367个布局和超过433k个物体实例，整合了三个不同的来源：真实世界扫描、专业CAD设计和程序生成的场景。每个布局都配有详细的结构文本，描述全局场景摘要、大型家具的相对位置以及小型物品的精细安排。这种多样且标注丰富的资源使模型能够学习各种室内环境中的复杂空间和语义模式。为了评估M3DLayout的潜力，我们建立了基准，使用文本条件扩散模型和文本条件自回归模型。实验结果表明，我们的数据集为训练布局生成模型提供了坚实的基础。其多源组成增强了多样性，特别是在Inf3DLayout子集中，提供了丰富的小型物体信息，使生成的场景更加复杂和详细。我们希望M3DLayout能成为推动文本驱动3D场景合成研究的重要资源。所有数据集和代码将在接受后公开。
英文摘要：
In text-driven 3D scene generation, object layout serves as a crucial intermediate representation that bridges high-level language instructions with detailed geometric output. It not only provides a structural blueprint for ensuring physical plausibility but also supports semantic controllability and interactive editing. However, the learning capabilities of current 3D indoor layout generation models are constrained by the limited scale, diversity, and annotation quality of existing datasets. To address this, we introduce M3DLayout, a large-scale, multi-source dataset for 3D indoor layout generation. M3DLayout comprises 21,367 layouts and over 433k object instances, integrating three distinct sources: real-world scans, professional CAD designs, and procedurally generated scenes. Each layout is paired with detailed structured text describing global scene summaries, relational placements of large furniture, and fine-grained arrangements of smaller items. This diverse and richly annotated resource enables models to learn complex spatial and semantic patterns across a wide variety of indoor environments. To assess the potential of M3DLayout, we establish a benchmark using both a text-conditioned diffusion model and a text-conditioned autoregressive model. Experimental results demonstrate that our dataset provides a solid foundation for training layout generation models. Its multi-source composition enhances diversity, notably through the Inf3DLayout subset which provides rich small-object information, enabling the generation of more complex and detailed scenes. We hope that M3DLayout can serve as a valuable resource for advancing research in text-driven 3D scene synthesis. All dataset and code will be made public upon acceptance.
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。
