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.
# 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.
