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--- |
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license: cc-by-nc-4.0 |
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task_categories: |
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- image-to-3d |
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language: |
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- en |
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tags: |
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- 3d-front |
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- 3d |
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- high-quality |
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- 3d-scene |
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--- |
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# 3D-Front (MIDI-3D) |
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[Github](https://github.com/VAST-AI-Research/MIDI-3D) | [Project Page](https://huanngzh.github.io/MIDI-Page/) | [Paper](https://arxiv.org/abs/2412.03558) | [Original Dataset](https://tianchi.aliyun.com/specials/promotion/alibaba-3d-scene-dataset) |
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## 1. Dataset Introduction |
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**TL;DR:** This dataset processes [3D-Front](https://tianchi.aliyun.com/specials/promotion/alibaba-3d-scene-dataset) into organized 3d scenes paired with rendered multi-view images and surfaces, which are used in [MIDI-3D](https://github.com/VAST-AI-Research/MIDI-3D). Each scene contains: |
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* 3D models (`.glb`) |
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* Point cloud (`.npy`) |
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* Rendered multi-view images in RGB, depth, normal, with camera information |
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## 2. Data Extraction |
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```bash |
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sudo apt-get install git-lfs |
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git lfs install |
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git clone https://huggingface.co/datasets/huanngzh/3D-Front |
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cat 3D-FRONT-SURFACE.part* > 3D-FRONT-SURFACE.tar.gz |
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cat 3D-FRONT-SCENE.part* > 3D-FRONT-SCENE.tar.gz |
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tar -xzvf 3D-FRONT-SURFACE.tar.gz |
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tar -xzvf 3D-FRONT-SCENE.tar.gz |
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tar -xzvf 3D-FRONT-RENDER.tar.gz |
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``` |
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If you just want to evaluate your model, you can download only the files containing the `test` keyword. |
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## 3. File Structure |
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```bash |
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3D-Front |
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├── 3D-FRONT-RENDER # rendered views |
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│ ├── 0a8d471a-2587-458a-9214-586e003e9cf9 # house |
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│ │ ├── Hallway-1213 # room |
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│ │ ... |
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├── 3D-FRONT-SCENE # 3d models (glb) |
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│ ├── 0a8d471a-2587-458a-9214-586e003e9cf9 # house |
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│ │ ├── Hallway-1213 # room |
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│ │ │ ├── Table_e9b6f54f-1d29-47bf-ba38-db51856d3aa5_1.glb # object |
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│ │ │ ... |
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├── 3D-FRONT-SURFACE # point cloud (npy) |
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│ ├── 0a8d471a-2587-458a-9214-586e003e9cf9 # house |
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│ │ ├── Hallway-1213 # room |
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│ │ │ ├── Table_e9b6f54f-1d29-47bf-ba38-db51856d3aa5_1.npy # object |
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│ │ │ ... |
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├── valid_room_ids.json # scene list |
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├── valid_furniture_ids.json # object list |
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├── midi_room_ids.json # scene list (subset used in midi) |
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└── midi_furniture_ids.json # object list (subset used in midi) |
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``` |
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About `room_ids` and `furniture_ids`: The i-th room in `room_ids` contains the objects whose ids are the i-th list in `furniture_ids`. |
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## 4. About Train and Test Set |
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MIDI uses **the last 1,000 rooms** in `midi_room_ids.json` as the **testset**, and the others as training set. |
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## Citation |
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If you find this dataset useful, please cite: |
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```bash |
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@article{huang2024midi, |
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title={MIDI: Multi-Instance Diffusion for Single Image to 3D Scene Generation}, |
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author={Huang, Zehuan and Guo, Yuan-Chen and An, Xingqiao and Yang, Yunhan and Li, Yangguang and Zou, Zi-Xin and Liang, Ding and Liu, Xihui and Cao, Yan-Pei and Sheng, Lu}, |
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journal={arXiv preprint arXiv:2412.03558}, |
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year={2024} |
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} |
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@inproceedings{fu20213d, |
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title={3d-front: 3d furnished rooms with layouts and semantics}, |
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author={Fu, Huan and Cai, Bowen and Gao, Lin and Zhang, Ling-Xiao and Wang, Jiaming and Li, Cao and Zeng, Qixun and Sun, Chengyue and Jia, Rongfei and Zhao, Binqiang and others}, |
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booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision}, |
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pages={10933--10942}, |
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year={2021} |
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} |
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@article{fu20213d, |
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title={3d-future: 3d furniture shape with texture}, |
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author={Fu, Huan and Jia, Rongfei and Gao, Lin and Gong, Mingming and Zhao, Binqiang and Maybank, Steve and Tao, Dacheng}, |
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journal={International Journal of Computer Vision}, |
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pages={1--25}, |
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year={2021}, |
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publisher={Springer} |
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} |
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``` |
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## Contact |
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[huangzehuan@buaa.edu.cn](mailto:huangzehuan@buaa.edu.cn) |
