File size: 6,380 Bytes
89f2fc0 33d68b0 89f2fc0 33d68b0 89f2fc0 5fd4000 33d68b0 5fd4000 33d68b0 5fd4000 33d68b0 5fd4000 33d68b0 5fd4000 33d68b0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 | ---
language:
- en
license: cc-by-4.0
size_categories:
- 100K<n<1M
task_categories:
- depth-estimation
- other
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-*
tags:
- computer-vision
- robustness
- image-corruption
- optical-flow
- scene-flow
- stereo
---
# RobustSpring: Benchmarking Robustness to Image Corruptions for Optical Flow, Scene Flow and Stereo
[Paper](https://huggingface.co/papers/2505.09368) | [Project Page](https://spring-benchmark.org) | [Code](https://github.com/cv-stuttgart/sceneflow_from_blender)
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 (DARUS)](https://doi.org/10.18419/DARUS-5047)
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"]))
```
---
## Citation
If you use this dataset, please cite the following papers:
```bibtex
@article{schmalfuss2025robustspring,
title={RobustSpring: Benchmarking Robustness to Image Corruptions for Optical Flow, Scene Flow and Stereo},
author={Schmalfuss, Jenny and Oei, Victor and Mehl, Lukas and Bartsch, Madlen and Agnihotri, Shashank and Keuper, Margret and Bruhn, Andr{\\'e}s},
journal={arXiv preprint arXiv:2505.09368},
year={2025}
}
@InProceedings{Mehl2023_Spring,
author = {Lukas Mehl and Jenny Schmalfuss and Azin Jahedi and Yaroslava Nalivayko and Andr\'es Bruhn},
title = {Spring: A High-Resolution High-Detail Dataset and Benchmark for Scene Flow, Optical Flow and Stereo},
booktitle = {Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2023}
}
```
## License
The RobustSpring dataset is licensed under CC-BY-4.0 |