---
license: apache-2.0
pretty_name: SouthKenSV
---

# SouthKensington Stereo Video Dataset

## Dataset Summary
This dataset provides stereo video sequences, captured using a ZED2 camera, designed for research in stereo video matching. 

## Dataset Structure
The folder stucture of the datasets should look like:

```none
/data/datasets/
            ├── SouthKensington
                ├── indoor
                    ├── video001
                    ├── ...
                        ├── images
                            ├── left
                                ├── left000000.png
                                ├── left000001.png
                            ├── right
                                ├── right000000.png
                                ├── right000001.png
                        ├── tracking_data1.txt
                ├── outdoor
                    ├── video001
                    ├── ...
```

### Data Instances

Each data instance corresponds to a single stereo frame in a video sequence and includes the following fields:

| Field           | Type      | Description                                           |
|-----------------|-----------|-------------------------------------------------------|
| `left`          | RGB image | Left view of the stereo pair (720×1280×3)             |
| `right`         | RGB image | Right view of the stereo pair (720×1280×3)            |
| `tracking_data` | TXT file  | Camera intrinsics and extrinsics per frame            |




### License 
The code is under MIT license.
SouthKensington SV dataset is licensed under Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International ([CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode)). 
Under this license, if you want to modify SK-SV or generate new data from SK-SV dataset (e.g., super-resolution, denoising, defocus), the releasing of your new data should be licensed under the same CC BY-NC-SA 4.0. 


## Citation Information
```
@article{jing2024match,
  title={Match Stereo Videos via Bidirectional Alignment},
  author={Junpeng Jing and Ye Mao and Anlan Qiu and Krystian Mikolajczyk},
  year={2024}
}
```