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Update dataset card with task category, paper link, and dataset structure (#2)
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---
license: bsd-3-clause
task_categories:
- depth-estimation
---
# What Makes Good Synthetic Training Data for Zero-Shot Stereo Matching? (WMGStereo)
[Paper](https://huggingface.co/papers/2504.16930) | [GitHub](https://github.com/princeton-vl/InfinigenStereo)
**WMGStereo** is a procedural dataset generator specifically optimized for zero-shot stereo matching performance. This repository contains the WMGStereo-150k dataset, a large-scale synthetic training dataset featuring indoor, nature, and dense "flying" scenes.
## Dataset Download
You can download the dataset using the `huggingface-cli`:
```bash
pip install huggingface-cli
huggingface-cli download pvl-lab/WMGStereo --repo-type dataset
```
## Dataset Structure
The dataset file structure is as follows:
```
.
└── WMGStereo/
├── indoor/
│ └── seed_num/
│ └── frames/
│ ├── Image/
│ │ ├── camera_0
│ │ └── camera_1
│ ├── camview/
│ │ ├── camera_0
│ │ └── camera_1
│ ├── disparity/
│ │ └── camera_0
│ ├── occ_mask/
│ │ └── camera_0
│ └── sky_mask/
│ └── camera_0
├── flying/
│ └── ...
└── nature/
└── ...
```
* **Camera 0 and 1**: correspond to left and right camera frames, respectively.
* **Ground Truth**: We provide disparity, occlusion, and sky-region masks for the left camera.
* **camview**: contains `.npz` files that contain a dictionary with indices `K`, `T`, `HW`, corresponding to calibration, translation, and resolution matrices.
## Citation
If you find WMGStereo useful for your work, please consider citing the academic paper:
```bibtex
@misc{yan2025proceduraldatasetgenerationzeroshot,
title={What Makes Good Synthetic Training Data for Zero-Shot Stereo Matching?},
author={David Yan and Alexander Raistrick and Jia Deng},
year={2025},
eprint={2504.16930},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2504.16930},
}
```