---
license: cc-by-4.0
viewer: false

tags:
- volumetric super-resolution
- 3D image analysis
- computed-tomography
- real-world super-resolution

task_categories:
- image-to-image

language:
- en

---


# VoDaSuRe: A Large-Scale Dataset Revealing Domain Shift in Volumetric Super-Resolution

## Dataset Summary

**VoDaSuRe** is a large-scale dataset for volumetric super-resolution (VSR), designed to study **domain shift between laboratory CT (Lab-CT) acquisitions**. The dataset is released in conjunction with the CVPR 2026 paper:

> *VoDaSuRe: A Large-Scale Dataset Revealing Domain Shift in Volumetric Super-Resolution*

The dataset consists of **32 volumetric scans of 16 samples**, each acquired under varying imaging conditions, enabling research on generalization, robustness, and cross-domain learning in 3D super-resolution.


## 🔗 Resources

* **Project page**: https://augusthoeg.github.io/VoDaSuRe/
* **Paper (arXiv)**: https://arxiv.org/abs/2603.23153
* **Code & pipelines**: https://github.com/AugustHoeg/VoxelSR


## Dataset Structure

The dataset is organized into **training and test splits**:

```
VoDaSuRe/
└── ome/
    ├── train/
    └── test/
```

Each split contains volumetric data stored in **OME-Zarr** format, a hierarchical and chunked format that enables efficient, lazy loading of large-scale volumetric data.


## Data Format (OME-Zarr)

Each sample is stored as a `.zarr` hierarchy with the following structure:

```
ome.zarr
├── HR  (High-resolution volume)
│   ├── 0  (full resolution)
│   ├── 1  (2× downsampled)
│   ├── 2  (4× downsampled)
│   └── 3  (8× downsampled)
│
├── LR  (Unregistered low-resolution volume)
│   ├── 0  (full resolution)
│   ├── 1  (2× downsampled)
│   ├── 2  (4× downsampled)
│   └── 3  (8× downsampled)
│
└── REG (Registered + intensity-matched low-resolution volume)
    ├── 0  (full resolution)
    └── 1  (2× downsampled)
```

### Modalities

* **HR**: High-resolution reference volumes
* **LR**: Low-resolution volumes (unregistered)
* **REG**: Registered and intensity-matched low-resolution volumes

## Dataset Size

* **Total size**: ~489 GB (compressed)
* **Disk requirement after extraction**: ~500 GB

⚠️ Ensure sufficient disk space before downloading.

## Download Instructions

You can download the dataset directly from the Hugging Face Hub:

https://huggingface.co/datasets/AugustHoeg/VoDaSuRe

### Python (recommended)

```python
from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="AugustHoeg/VoDaSuRe",
    repo_type="dataset"
)
```

### Git (with Git LFS)

```bash
git lfs install
git clone https://huggingface.co/datasets/AugustHoeg/VoDaSuRe
```

## Data Usage

The dataset is provided as compressed `.tar` archives containing `.zarr` folders.

To extract:

```bash
cd VoDaSuRe && bash extract_files.sh
```

After extraction, the dataset can be accessed using libraries supporting OME-Zarr, such as:

* `zarr`
* `ome-zarr-py`
* `dask`

### Example: Loading sample slices using zarr

Below is a minimal example demonstrating how to load and access slices from a single sample.

```python
import zarr

# Open a sample from the training split
z = zarr.open("ome/train/Bamboo_A_bin1x1_ome_1.zarr", mode="r")

# Visualize zarr store
print(z.tree())

# High-resolution slice
img_hr = z["HR/0"][1000, :, :]

# Registered low-resolution slice (4x resolution difference)
img_reg = z["REG/0"][250, :, :]

# Unregistered low-resolution slice
img_lr = z["LR/0"][1000, :, :]
```

### Notes

* Volumes are stored in (D, H, W) format, with the first dimension (`D`) corresponding to the slice index
* Resolution scales for each scan are available via levels 0-3 (`HR/1`, `HR/2`, etc.)

⚠️ Be careful with loading full volumes, as this may exceed system memory


## Intended Use

VoDaSuRe is designed for:

* Volumetric super-resolution (3D SR)
* Domain generalization and domain shift analysis
* Benchmarking learning-based SR methods under realistic acquisition scenarios

## Dataset Creation

The dataset was created by paired high- and low-resolution volumetric acquisition using Lab-CT.

Further details are available in the associated paper and project page.

## Citation

If you use this dataset, please cite our paper:

```bibtex
@article{hoeg2026vodasure,
  title={VoDaSuRe: A Large-Scale Dataset Revealing Domain Shift in Volumetric Super-Resolution},
  author={August Leander Høeg and Sophia Wiinberg Bardenfleth and Hans Martin Kjer and Tim Bjørn Dyrby and Vedrana Andersen Dahl and Anders Dahl},
  journal={Proceedings of the Computer Vision and Pattern Recognition Conference},
  year={2026},
  url={https://augusthoeg.github.io/VoDaSuRe/}
}
```

## Contact

For questions or issues, please open an issue in the GitHub repository:

https://github.com/AugustHoeg/VoxelSR