Datasets:
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README.md
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> *VoDaSuRe: A Large-Scale Dataset Revealing Domain Shift in Volumetric Super-Resolution*
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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.
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---
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## 🔗 Resources
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* **Project page**: https://augusthoeg.github.io/VoDaSuRe/
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* **Paper (arXiv)**: https://arxiv.org/abs/2603.23153
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* **Code & pipelines**: https://github.com/AugustHoeg/VoxelSR
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---
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## Dataset Structure
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```
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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.
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## Data Format (OME-Zarr)
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* **HR**: High-resolution reference volumes
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* **LR**: Low-resolution volumes (unregistered)
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* **REG**: Registered and intensity-matched low-resolution volumes
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## Dataset Size
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* **Disk requirement after extraction**: ~500 GB
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⚠️ Ensure sufficient disk space before downloading.
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---
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## Download Instructions
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git lfs install
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git clone https://huggingface.co/datasets/AugustHoeg/VoDaSuRe
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```
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## Data Usage
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* `zarr`
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* `ome-zarr-py`
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* `dask`
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## Intended Use
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* Volumetric super-resolution (3D SR)
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* Domain generalization and domain shift analysis
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* Benchmarking learning-based SR methods under realistic acquisition scenarios
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## Dataset Creation
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The dataset was created using **laboratory CT (Lab-CT) imaging systems**, capturing paired high- and low-resolution volumetric scans under varying acquisition conditions.
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Further details are available in the associated paper and project page.
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## Citation
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url={https://augusthoeg.github.io/VoDaSuRe/}
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}
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```
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---
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## Contact
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> *VoDaSuRe: A Large-Scale Dataset Revealing Domain Shift in Volumetric Super-Resolution*
|
| 12 |
|
| 13 |
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.
|
|
|
|
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---
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## 🔗 Resources
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* **Project page**: https://augusthoeg.github.io/VoDaSuRe/
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* **Paper (arXiv)**: https://arxiv.org/abs/2603.23153
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* **Code & pipelines**: https://github.com/AugustHoeg/VoxelSR
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---
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## Dataset Structure
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```
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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.
|
|
|
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---
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## Data Format (OME-Zarr)
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* **HR**: High-resolution reference volumes
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* **LR**: Low-resolution volumes (unregistered)
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* **REG**: Registered and intensity-matched low-resolution volumes
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---
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## Dataset Size
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* **Disk requirement after extraction**: ~500 GB
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⚠️ Ensure sufficient disk space before downloading.
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---
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## Download Instructions
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git lfs install
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git clone https://huggingface.co/datasets/AugustHoeg/VoDaSuRe
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```
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---
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## Data Usage
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* `zarr`
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* `ome-zarr-py`
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* `dask`
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---
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## Intended Use
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* Volumetric super-resolution (3D SR)
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* Domain generalization and domain shift analysis
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* Benchmarking learning-based SR methods under realistic acquisition scenarios
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---
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## Dataset Creation
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The dataset was created using **laboratory CT (Lab-CT) imaging systems**, capturing paired high- and low-resolution volumetric scans under varying acquisition conditions.
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Further details are available in the associated paper and project page.
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---
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## Citation
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url={https://augusthoeg.github.io/VoDaSuRe/}
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}
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```
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---
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## Contact
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