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license: cc-by-nc-4.0 |
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--- |
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# Structured3D-SpatialLM Dataset |
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Structured3D dataset preprocessed in SpatialLM format for layout estimation with LLMs. |
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## Overview |
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This dataset is derived from [Structured3D](https://structured3d-dataset.org/) **3,500 synthetic house designs** created by professional designers, preprocessed and formatted specifically for [SpatialLM](https://github.com/manycore-research/SpatialLM) training. |
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Point clouds and layouts are derived from the [RoomFormer](https://github.com/ywyue/RoomFormer/tree/main/data_preprocess) data preprocessing script. |
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## Data Extraction |
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Point clouds and layouts are compressed in zip files. To extract the files, run the following script: |
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```bash |
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cd structured3d-spatiallm |
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chmod +x extract.sh |
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./extract.sh |
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``` |
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## Dataset Strucutre |
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```bash |
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structured3d-spatiallm/ |
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├── structured3d_train.json # Training conversations |
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├── structured3d_test.json # Test conversations |
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├── dataset_info.json # Dataset metadata |
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├── split.csv # Train/val split mapping |
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├── pcd/ # Point cloud data |
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│ └── .ply |
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├── layout/ # Scene layout annotations |
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│ └── .txt |
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└── extract.sh # Extraction script |
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``` |
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The `structured3d_train.json` and `structured3d_test.json` dataset follows the **SpatialLM format** with ShareGPT-style conversations: |
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```json |
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{ |
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"conversations": [ |
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{ |
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"from": "human", |
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"value": "<point_cloud>Detect walls, doors, windows. The reference code is as followed: ..." |
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}, |
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{ |
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"from": "gpt", |
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"value": "<|layout_s|>wall_0=...<|layout_e|>" |
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} |
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], |
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"point_clouds": ["pcd/scene_id.ply"] |
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} |
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``` |
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## License |
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This dataset is derived from [Structured3D](https://github.com/bertjiazheng/Structured3D) dataset. Please refer to the original dataset's license terms for usage restrictions. |
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## Citation |
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If you use this dataset in your research, please cite the original Structured3D paper: |
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```bibtex |
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@inproceedings{Structured3D, |
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title = {Structured3D: A Large Photo-realistic Dataset for Structured 3D Modeling}, |
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author = {Jia Zheng and Junfei Zhang and Jing Li and Rui Tang and Shenghua Gao and Zihan Zhou}, |
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booktitle = {Proceedings of The European Conference on Computer Vision (ECCV)}, |
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year = {2020} |
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} |
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``` |
