CoolFace
Datasetpublic

LForster/Sintel-Low-light-Noise

Sintel Low-light Noise ELD A synthetic low-light optical flow dataset derived from MPI Sintel using the ELD low-light noise preset. This dataset contains noisy RGB frames for both the train and test splits and is intended for: optical flow robustness evaluation in low-light conditions fine-tuning pretrained optical flow models controlled experiments on synthetic low-light degradation Contents The dataset contains ELD-corrupted Sintel frames for: training… See the full description on the dataset page: https://huggingface.co/datasets/LForster/Sintel-Low-light-Noise.

sourceHugging Faceapache-2.0updated 6mo agoView on Hugging Face
1likes51downloads
Dataset Card

# Sintel Low-light Noise ELD

A synthetic low-light optical flow dataset derived from MPI Sintel using the ELD low-light noise preset.

This dataset contains noisy RGB frames for both the train and test splits and is intended for:

  • optical flow robustness evaluation in low-light conditions
  • fine-tuning pretrained optical flow models
  • controlled experiments on synthetic low-light degradation

## Contents

The dataset contains ELD-corrupted Sintel frames for:

  • training
  • test

This dataset includes only the noisy ELD data.

## What This Dataset Is For

This dataset is useful when you want to test or train optical flow models on darker, noisier Sintel-style inputs without changing the underlying scene content.

Typical use cases:

  • compare model performance on standard vs low-light inputs
  • fine-tune a pretrained model for low-light robustness
  • benchmark robustness under synthetic low-light degradation

## Noise Model

This dataset uses the ELD low-light noise model.

The ELD corruption includes:

  • brightness reduction
  • shot noise
  • read noise
  • quantization noise
  • banding artifacts

Compared with more aggressive synthetic corruption models, ELD generally produces more stable and visually plausible low-light results.

## Why Sintel + ELD?

MPI Sintel is widely used for optical flow evaluation, but it does not natively include low-light variants.

Applying ELD-style degradation provides:

  • a controlled robustness benchmark
  • the same scene/layout content as Sintel
  • a direct way to study low-light failure modes in optical flow

## Recommended Use

Best use:

  • fine-tune a pretrained optical flow model
  • evaluate robustness to low-light corruption
  • compare against clean Sintel performance

Less recommended:

  • treating this as real-world low-light ground truth
  • relying on it as the only low-light training source

This dataset is synthetic and is best used for controlled experiments.

## File Structure

  Sintel-noisy/
    train/
      alley_1/
      alley_2/
      ...
    test/
      ambush_1/
      cave_3/
      ...

## Notes

  • This dataset contains only the ELD noisy version.
  • It is a synthetic low-light corruption dataset, not a real capture dataset.
  • Transfer to real low-light video should be validated separately.

## Acknowledgements

This dataset is derived from MPI Sintel and applies synthetic ELD low-light corruption to the original image content.

Please respect the licensing terms of the original Sintel dataset.