Abstract
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 15,080 layouts and over 258k 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 a text-conditioned diffusion 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.
Abstract (translated)
在基于文本驱动的三维场景生成中，物体布局作为一种关键的中间表示形式，连接了高层次的语言指令和详细的几何输出。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式编辑。然而，当前三维室内布局生成模型的学习能力受到现有数据集规模有限、多样性不足以及标注质量不高的限制。为了解决这些问题，我们引入了M3DLayout，这是一个大规模的多源数据集，用于三维室内布局生成。M3DLayout包含15,080个布局和超过258k个物体实例，整合了三个不同的来源：真实世界的扫描、专业CAD设计以及程序化生成场景。每个布局都配有一份详细的结构化文本描述，包括全局场景概要、大型家具的相对位置关系以及小型物品的精细布置安排。这种多样性和丰富标注的数据资源使模型能够学习到复杂的空间和语义模式，在各种室内环境中具有广泛的适用性。 为了评估M3DLayout的潜力，我们使用基于条件文本扩散模型建立了一个基准测试平台。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。其多源构成增强了多样性，特别是Inf3DLayout子集提供的丰富的小物体信息，使更复杂和详细的场景生成成为可能。我们希望M3DLayout能够作为一个有价值的资源，推动基于文本驱动的三维场景合成研究的进步。
URL
https://arxiv.org/abs/2509.23728
