---
license: cc-by-4.0
dataset_info:
  features:
  - name: image1_path
    dtype: string
  - name: image2_path
    dtype: string
  - name: image1s_path
    dtype: string
  - name: image2s_path
    dtype: string
  - name: corruption
    dtype: string
  - name: split
    dtype: string
  - name: scene_id
    dtype: string
  - name: frame_leftright
    dtype: string
  - name: frame_forwardbackward
    dtype: string
  - name: index
    dtype: int32
  - name: sample_type
    dtype: string
  splits:
  - name: test
    num_bytes: 34406100
    num_examples: 158800
  download_size: 2757713
  dataset_size: 34406100
configs:
- config_name: default
  data_files:
  - split: test
    path: data/test-*
language:
- en
tags:
- computer-vision
- robustness
- image-corruption
- optical-flow
- scene-flow
- stereo
size_categories:
- 100K<n<1M
---

# RobustSpring: Benchmarking Robustness to Image Corruptions for Optical Flow, Scene Flow and Stereo

This dataset provides structured **metadata only** for the [RobustSpring](https://spring-benchmark.org) dataset. All image samples are referenced by relative file paths, and must be paired with local image data downloaded separately from the public release site.

* **Dataset on the Hub**: [jeschmalfuss/RobustSpring](https://huggingface.co/datasets/jeschmalfuss/RobustSpring)
* **Image Data**: [RobustSpring](https://doi.org/10.18419/DARUS-5047)

For the related [research](https://www.arxiv.org/abs/2505.09368) see
```
RobustSpring: Benchmarking Robustness to Image Corruptions for Optical Flow, Scene Flow and Stereo
Jenny Schmalfuss*, Victor Oei*, Lukas Mehl, Madlen Bartsch, Shashank Agnihotri, Margret Keuper, Andrés Bruhn
https://doi.org/10.48550/arXiv.2505.09368
```
RobustSpring is an image-corruption dataset for optical flow, scene flow and stereo, that applies 20 different image corruption to the test split of the [Spring](https://spring-benchmark.org) dataset.
The combined Spring and RobustSpring website is at [spring-benchmark.org](https://spring-benchmark.org)

---

## Dataset Overview

Each sample in this dataset represents one data sample on which to predict:

- **Optical Flow**
- **Scene Flow**
- **Stereo Disparity**

The dataset contains only **file paths** to local image files. The raw image data must be downloaded separately.

---

## Download Image Data

Please download the raw image data zips files from:

**https://doi.org/10.18419/DARUS-5047**

After downloading:
1. Extract all contents to a local `data/` folder.
2. Ensure the folder structure looks like:

```
/data/
  brightness/
    test/
      scene_0003/
        frame_left/
          frame_left_0001.png
          frame_left_0002.png
          ...
        frame_right/
          frame_right_0001.png
          frame_right_0002.png
          ...
      scene_0019/
        frame_left/
          ...
        frame_right/
          ...
      scene_0028
      ...
  contrast/
    test/
      scene_0003/
      scene_0019/
      ...
  defocus_blur/
    test/
      scene_0003/
      scene_0019/
      ...
  ...
```

---

## Dataset Structure

Each sample in the dataset includes:

| Field                   | Type     | Description                                                             |
|---------------          |----------|-------------------------------------------------                        |
| `sample_type`           | `string` | `"optic-flow"`, `"scene-flow"` or `"stereo"`                            |
| `corruption`            | `string` | Image corruption type                                                   |
| `split`                 | `string` | Dataset split. `test` for all data.                                     |
| `scene_id`              | `string` | Spring's scene ID                                                       |
| `frame_leftright`       | `string` | If data is centered on left or right stereo frame                       |
| `frame_forwardbackward` | `string` | For optic- and scene-flow. Forward or backward in time.                 |
| `index`                 | `int32`  | Data sample index. Own indices for optical flow, scene flow and stereo. |
| `image1_path`           | `string` | Relative path to pivot image                                            |
| `image2_path`           | `string` | Relative path to pivot image at next time step (OF & SF only)           |
| `image1s_path`          | `string` | Relative path to stereo of pivot image (SF and S only)                  |
| `image2s_path`          | `string` | Relative path to stereo of image at next time step (SF only)            |

No image content is stored. Paths only.

---

## How to Use

### 1. Install Dependencies

```bash
pip install datasets Pillow
```

### 2. Load the Dataset

```python
from datasets import load_dataset

dataset = load_dataset("jeschmalfuss/RobustSpring", split="test")  # all samples
```

## 3. Filtering by Data Type

You can filter the dataset to only retrieve the type of samples you're interested in: optical flow, scene flow or stereo.


```python
dataset_optic_flow = dataset.filter(lambda x: x["sample_type"] == "optic-flow")
dataset_scene_flow = dataset.filter(lambda x: x["sample_type"] == "scene-flow")
dataset_stereo     = dataset.filter(lambda x: x["sample_type"] == "stereo")
```

### 4. Set Local Path to Images

```python
import os
from PIL import Image

base_path = "/absolute/path/to/data"  # where you extracted the downloaded zip

sample = dataset_optic_flow[0]
img1 = Image.open(os.path.join(base_path, sample["image1_path"]))
img2 = Image.open(os.path.join(base_path, sample["image2_path"]))
```


---


## License

The RobustSpring dataset is licensed under CC-BY-4.0