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AffordSplat

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AffordSplat
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---
language:
- en
size_categories:
- 10K<n<100K
ArXiv:
- https://arxiv.org/abs/2504.11218
---

<!-- Provide a quick summary of the dataset. -->

In this repository, we present 3DAffordSplat, the first large-scale, multi-modal dataset tailored for 3DGS-based affordance reasoning. This dataset includes 23k Gaussian instances, 8k point cloud instances, and 6k manually annotated affordance labels, encompassing 21 object categories and 18 affordance types.

# Dataset Structure

After downloading, the data structure should be as follows:
```
—Seen
    ├── train
    │   ├── bag
    │   │   ├── Gaussian
    │   │   │   └── GS_0017.ply
    │   │   │       ......
    │   │   ├── PointCloud
    │   │   │   └── PC_0001.ply
    │   │   │       ......
    │   │   ├── contain
    │   │   │   ├── GS_anno_0017.ply
    │   │   │   ├── PC_anno_0001.json
    │   │   │       ......
    │   │   └── grasp
    │   │       ......
    │   └── bed
    │       ......
    │
    ├── val
    │   ├── bag
    │   │   ├── Gaussian
    │   │   │   └── GS_0009.ply
    │   │   │       ......
    │   │   ├── contain
    │   │   │   └── GS_anno_0009.ply
    │   │   │       ......
    │   │   └── grasp
    │   │       ......
    │   └── bed
    │       ......
    │
    └── test
        ├── bag
        │   ├── Gaussian
        │   │   └── GS_0001.ply
        │   │       ......
        │   ├── contain
        │   │   └── GS_anno_0001.ply
        │   │       ......
        │   └── grasp
        │       ......
        └── bed
            ......

—Affordance-Question.csv
—obj_aff_structure.json
—UnSeen_test.json
—UnSeen_train.json
```

# Dataset Details
For more information on detailed statistics and the methodology of AffordSplat, please refer to the following resources:
- **Repository:** [Github Repository](https://github.com/HCPLab-SYSU/3DAffordSplat)
- **Paper:** [3DAffordSplat: Efficient Affordance Reasoning with 3D Gaussians](https://arxiv.org/abs/2504.11218)
<!-- - **Demo [optional]:** [More Information Needed] -->

Additionally, we sincerely thank Guantian Liu, Yao Xiao, Xinyu Li, Kecheng Liang and Yipeng Ouyang for their contributions.

# Contact
This project is for research purpose only, please contact us for the licence of commercial use. For any other questions please contact (weizm6@mail2.sysu.edu.cn, linjy279@mail2.sysu.edu.cn or liuy856@mail.sysu.edu.cn).

# Citation
If you use this data, please cite our paper.
```
@misc{wei20253daffordsplatefficientaffordancereasoning,
      title={3DAffordSplat: Efficient Affordance Reasoning with 3D Gaussians}, 
      author={Zeming wei and Junyi Lin and Yang Liu and Weixing Chen and Jingzhou Luo and Guanbin Li and Liang Lin},
      year={2025},
      eprint={2504.11218},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2504.11218}, 
}
```


# Acknowledgement
The construction of AffordSplat dataset is based on [3DAffordanceNet](https://github.com/Gorilla-Lab-SCUT/AffordanceNet), [LASO](https://github.com/yl3800/laso) and [ShapeSplat](https://huggingface.co/datasets/ShapeSplats/ModelNet_Splats). We sincerely thank them for their contributions to the community.