# UnderWaterNet Dataset

Official repository for the UnderWaterNet dataset, a comprehensive collection of underwater and surface images for computer vision tasks including 2D object detection and 6D pose estimation.

## Paper

**[Model-Based Underwater 6D Pose Estimation From RGB](https://ieeexplore.ieee.org/abstract/document/10265120)**  
Published in IEEE Xplore

## Official Website

[https://sapienzadavide.github.io/uwpose.github.io/index.html](https://sapienzadavide.github.io/uwpose.github.io/index.html)

## Dataset Structure

### Real Underwater & Surface Images (UWds)

Compressed archives of real-world images across different environmental conditions:

- `UW6d/asphalt.tar.gz` (4.6GB) - Asphalt surface images
- `UW6d/drydirt.tar.gz` (3.4GB) - Dry dirt surface images  
- `UW6d/dryshadow.tar.gz` (2.5GB) - Shadowed dry surface images
- `UW6d/drywhite.tar.gz` (3.8GB) - White dry surface images
- `UW6d/grass.tar.gz` (4.5GB) - Grass surface images
- `UW6d/uwLED.tar.gz` (530MB) - Underwater LED-lit images
- `UW6d/uwshadow.tar.gz` (1.9GB) - Underwater shadowed images
- `UW6d/w_p_occluded.tar.gz` (956MB) - Water with partial occlusion

### 3D Object Models (UW6d)

- `UW6d/objects_ply/` - PLY mesh files for 6D pose estimation tasks

### 2D Object Detection Dataset (UW2d)

Real-world images for 2D object detection:

- `UW2d/cube.tar.gz` - Cube object images
- `UW2d/cup.tar.gz` - Cup object images
- `UW2d/hotstab.tar.gz` - Hotstab object images
- `UW2d/jar.tar.gz` - Jar object images
- `UW2d/multiple_objects.tar.gz` - Multi-object scenes

### Synthetic Datasets

Unity-generated synthetic data for training:

- `synthetic_2d.tar.gz` - Synthetic dataset for 2D object detection (no background)
- `synthetic_6d.tar.gz` - Synthetic dataset for 6D pose estimation (sandy background)

## Tasks Supported

- 6D pose estimation
- 2D object detection

## Citation

If you use this dataset in your research, please cite:
```bibtex
@ARTICLE{10265120,
  author={Sapienza, Davide and Govi, Elena and Aldhaheri, Sara and Bertogna, Marko and Roura, Eloy and Pairet, Èric and Verucchi, Micaela and Ardón, Paola},
  journal={IEEE Robotics and Automation Letters}, 
  title={Model-Based Underwater 6D Pose Estimation From RGB}, 
  year={2023},
  volume={8},
  number={11},
  pages={7535-7542},
  keywords={Pose estimation;Three-dimensional displays;Sensors;Solid modeling;Task analysis;Cameras;Robot sensing systems;Computer vision for manipulation;dataset;manipulation;pose estimation;underwater},
  doi={10.1109/LRA.2023.3320028}
  }

```

## License

openrail