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)

from huggingface_hub import snapshot_download

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

Git (with Git LFS)

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:

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.

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:

@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