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| license: cc-by-nc-4.0 |
| language: |
| - en |
| size_categories: |
| - 100K<n<1M |
| --- |
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| # M3DLayout: A Multi-Source Dataset of 3D Indoor Layouts and Structured Descriptions for 3D Generation |
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| **We are continuously scaling up our layout collection and will release more results as soon as they are ready. Please stay tuned and follow our work for updates!** |
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| 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. |
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| 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. |
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| 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. |
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| ## Dataset Description |
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| The dataset is separated into 3 parts: |
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| | Type | Description | Content | Size | |
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| | `scene_dataset` | Origin scene with separated object geometries and textures | • Infinigen (8392 rooms with normal object density + 7607 rooms with relatively low object density = 15999 rooms): scene.blend, scene with segemented objects and textures. <br>• Matterport3D (90 houses): Postprocessed by segmenting each house into separated ply objects, you can import each subdir as a whole to Blender to receive a complete scene.<br>• 3D-Front (5173 rooms): Since we have not applied any additional processing, you can download the original 3D-FRONT dataset directly from [3D-Front official link](https://huggingface.co/datasets/huanngzh/3D-Front) | 3T before uncompress | |
| | `rendering_dataset` | Rendered Images from scene | • Infinigen (15864 rooms): Floor masks, Oblique-view scene renderings, Top-down scene renderings, Text descriptions, Detailed per-scene JSON <br>• Matterport3D (90 houses + 4 json files + 1 README.md): Floor masks for each room, Top-down layout renderings for each room, Multi-Level Detailed per-scene JSON <br>• 3D-Front (5173 rooms): Floor masks, Top-down scene renderings | 250GB before uncompress | |
| | `layout_dataset` | Layout extracted from scene | **`<data_source>_train.json`**, **`<data_source>_test.json`**, and **`<data_source>_val.json`** for Infinigen & Matterport3D & 3D-Front. Including object count, category, location, bbox size, rotation, multi-level detailed description, etc. | 31MB before uncompress | |
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| To be simple, |
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| If you want to do Scene Generation/Understanding/Reconstruction, Embodied AI and so on, you can directly download the **`scene_dataset`**. Moreover, you can extract point cloud or do further Detection, Segmentation or Editing tasks since all objects in the scene are clearly separated. |
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| If you want to do some image/text to layout/scene or some 2D tasks, you can download **`rendering_dataset`**. |
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| If you want to utilize the intermediate scene layout for your downstream research, you can download **`layout_dataset`**. |
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| We have provided abundant functions in `render.py`, `util.py` and `visualization_mlayout.py` from [Object-Retrieval-Layout2Scene](https://github.com/Graphic-Kiliani/Object-Retrieval-Layout2Scene/tree/432d4c22dbd2d16e09d6c81629f124e523f0dc6a) to postprocess (visualize/filter/rendering etc. ) the infinigen scene data. |
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| ## Correlated Linkage |
| - **Github Repository:** https://github.com/Graphic-Kiliani/M3DLayout-code |
| - **Paper:** https://arxiv.org/abs/2509.23728 |
| - **Project Page:** https://graphic-kiliani.github.io/M3DLayout/ |
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| ## Citation |
| If you find this dataset useful, please cite: |
| ```bibtex |
| @article{zhang2025m3dlayout, |
| title={M3DLayout: A Multi-Source Dataset of 3D Indoor Layouts and Structured Descriptions for 3D Generation}, |
| author={Yiheng, Zhang and Zhuojiang, Cai and Mingdao, Wang and Meitong, Guo and Tianxiao, Li and Li, Lin and Yuwang, Wang}, |
| journal={arXiv preprint arXiv:2509.23728}, |
| year={2025}, |
| url={https://arxiv.org/abs/2509.23728}, |
| } |
| ``` |
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| ## Dataset Card Contact |
| If you have any question about our dataset or seek for any form of collaboration, feel free to contact 'e1349382@u.nus.com'. |
