Underwater imaging is fundamentally challenging due to wavelength-dependent light attenuation, strong scattering from suspended particles, turbidity-induced blur, and nonuniform illumination. These effects impair standard cameras and make ground-truth motion nearly impossible to obtain. On the other hand, event cameras offer microsecond resolution and high dynamic range. Nonetheless, progress on investigating event cameras for underwater environments has been limited due to the lack of datasets that pair realistic underwater optics with accurate optical flow. To address this problem, we introduce the first synthetic underwater event-based optical flow (UEOF) dataset derived from physically-based ray-traced RGBD sequences. Using a modern video-to-event pipeline applied to rendered underwater videos, we produce realistic event data streams with dense ground-truth flow, depth, and camera motion. Moreover, we benchmark state-of-the-art learning-based and model-based optical flow prediction methods to understand how underwater light transport affects event formation and motion estimation accuracy. The UEOF dataset establishes a new baseline for future development and evaluation of underwater event-based perception algorithms.
If you find this project useful, then please consider citing both our paper and dataset.
@article{truong2026ueof,
title={UEOF: A benchmark dataset for underwater event-based optical flow},
author={Truong, Nick and Karmokar, Pritam P and Beksi, William J},
journal={arXiv preprint arXiv:2601.10054},
year={2026}
}@data{mavmatrix/dataset.xxx,
title={{UEOF}},
author={Truong, Nick and Karmokar, Pritam P and Beksi, William J},
publisher={MavMatrix},
version={V1},
url={https://doi.org/xxx/dataset.xxx},
doi={xxx/dataset.xxx},
year={2026}
}The source code associated with this project is licensed under the Apache License, Version 2.0. The UEOF dataset is available for non-commercial use under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License ("CC BY-NC-SA 4.0").