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ageppert/single_photon_challenge_full_preprocessed

Single Photon Challenge — Full Preprocessed Dataset Preprocessed measurement/target PNG pairs derived from the Single Photon Challenge reconstruction dataset. Source The raw dataset (~425GB training, ~42GB test) is hosted by the WISION Lab at UW-Madison. Photoncubes contain 1024 binary frames from a simulated single-photon camera, paired with ground-truth RGB reconstructions. Challenge website: https://singlephotonchallenge.com/ Download page:… See the full description on the dataset page: https://huggingface.co/datasets/ageppert/single_photon_challenge_full_preprocessed.

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Single Photon Challenge — Full Preprocessed Dataset

Preprocessed measurement/target PNG pairs derived from the Single Photon Challenge reconstruction dataset.

Source

The raw dataset (~425GB training, ~42GB test) is hosted by the WISION Lab at UW-Madison. Photoncubes contain 1024 binary frames from a simulated single-photon camera, paired with ground-truth RGB reconstructions.

—Challenge website: <https://singlephotonchallenge.com/>
—Download page: <https://singlephotonchallenge.com/download>
—VisionSIM toolkit: <https://visionsim.readthedocs.io/>

Preprocessing pipeline

Each photoncube was preprocessed using the same approach as the challenge FAQ naive sum:

1.Average the last 16 binary frames → detection probability in [0, 1]
2.Invert SPC response (invert_response=True, factor=0.5) → linear RGB flux via flux = -log(1 - p) / factor
3.sRGB tonemap (tonemap=True) → standard gamma curve
4.Save as uint8 PNG

Measurements and targets are stored as 800×800 RGB PNGs.

Dataset statistics

Split	Measurements	Targets	Paired
train	1850	1850	yes
test	185	0	no (test set has no ground truth)
total	2035	1850

Directory structure

single_photon_challenge_full_preprocessed/
  metadata.json
  train/
    <scene>/<frame>_measurement.png
    <scene>/<frame>_target.png
  test/
    <scene>/<frame>_measurement.png

Usage

python

from huggingface_hub import snapshot_download

# Download the full preprocessed dataset
root = snapshot_download(
    repo_id="ageppert/single_photon_challenge_full_preprocessed",
    repo_type="dataset",
)

# Or use with the diffusion training codebase:
# Set in config.py:
#   PREPROCESSED_DATA_CONFIG["dataset_source"] = "hf"
#   PREPROCESSED_DATA_CONFIG["dataset_hf_repo"] = "ageppert/single_photon_challenge_full_preprocessed"

Preprocessing parameters

json

{
  "source": "Single Photon Challenge reconstruction dataset",
  "source_url": "https://singlephotonchallenge.com/download",
  "num_frames": 16,
  "invert_response": true,
  "invert_factor": 0.5,
  "tonemap": true,
  "split": "all",
  "notes": "Measurements are preprocessed from raw photoncubes using: naive sum averaging, SPC response inversion, and sRGB tonemapping. Saved as uint8 PNGs. Targets are copied from original ground-truth PNGs."
}

Citation

If you use this dataset, please cite the Single Photon Challenge:

@misc{singlephotonchallenge,
    title={The Single Photon Challenge},
    author={Jungerman, Sacha and Ingle, Atul and Nousias, Sotiris and Wei, Mian and White, Mel and Gupta, Mohit},
    year={2025},
    url={https://singlephotonchallenge.com/}
}