Added usage instructions to readme

README.md

@@ -46,4 +46,149 @@ tags:
 - 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