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---
license: openrail
task_categories:
- object-detection
- robotics
tags:
- underwater
- 6d-pose-estimation
- computer-vision
- synthetic
- unity
size_categories:
- 10K<n<100K
pretty_name: UnderWaterNet Dataset
---
# 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
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