This repo is a work in progress
Official Repository for OpenWaters: Photorealistic Simulations for Underwater Computer Vision
OpenWaters is a simulation tool made in Unreal Engine for generating massive underwater computer vision datasets. OpenWaters supports creation of massive amount of underwater images by emulating diverse real-world conditions. It allows for fine controls over every variable in a simulation instance, including geometry, rendering parameters like ray-traced water caustics, scattering and ground-truth labels.
RELEASE version: 1.00
| Item | Link |
|---|---|
| OpenWaters v1.00 Project Files (Must run on NvRTX UE4.26-Caustics) | Coming Soon |
| OpenWaters-Depth Dataset | Coming Soon |
Contact: If you have questions or comments (or bugs!) please open a github issue or contact me at: mehdimousavi.redcap[at]gmail[dot]com
If you end up using OpenWaters or The Neural Networks, please cite our paper:
@inproceedings{10.1145/3491315.3491336,
author = {Mousavi, Mehdi and Vaidya, Shardul and Sutradhar, Razat and Ashok, Ashwin},
title = {OpenWaters: Photorealistic Simulations For Underwater Computer Vision},
year = {2021},
isbn = {9781450395625},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3491315.3491336},
doi = {10.1145/3491315.3491336},
abstract = { In this paper, we present OpenWaters, a real-time open-source underwater simulation kit for generating photorealistic underwater scenes. OpenWaters supports creation of massive amount of underwater images by emulating diverse real-world conditions. It allows for fine controls over every variable in a simulation instance, including geometry, rendering parameters like ray-traced water caustics, scattering, and ground-truth labels. Using underwater depth (distance between camera and object) estimation as the use-case, we showcase and validate the capabilities of OpenWaters to model underwater scenes that are used to train a deep neural network for depth estimation. Our experimental evaluation demonstrates depth estimation using synthetic underwater images with high accuracy, and feasibility of transfer-learning of features from synthetic to real-world images. },
booktitle = {The 15th International Conference on Underwater Networks & Systems},
articleno = {3},
numpages = {5},
location = {Shenzhen, Guangdong, China},
series = {WUWNet'21}
}