# UEOF: A Benchmark Dataset for Underwater Event-Based Optical Flow

arXiv: https://arxiv.org/abs/2601.10054 · HTML: https://mlsanity.com/paper/2601.10054/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 25 of its 62 references that are in the map.
- First posted on arXiv 15 Jan 2026.

## Builds on (most cited first, 25 of 25)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [AirSim: High-Fidelity Visual and Physical Simulation for Autonomous Vehicles](https://mlsanity.com/paper/1705.05065/) (2017)
- [DSEC: A Stereo Event Camera Dataset for Driving Scenarios](https://mlsanity.com/paper/2103.06011/) (2021)
- [E-RAFT: Dense Optical Flow from Event Cameras](https://mlsanity.com/paper/2108.10552/) (2021)
- [Secrets of Event-Based Optical Flow](https://mlsanity.com/paper/2207.10022/) (2022)
- [SeaSplat: Representing Underwater Scenes with 3D Gaussian Splatting and a Physically Grounded Image Formation Model](https://mlsanity.com/paper/2409.17345/) (2024)
- [BlinkFlow: A Dataset to Push the Limits of Event-based Optical Flow Estimation](https://mlsanity.com/paper/2303.07716/) (2023)
- [Learning Optical Flow from Event Camera with Rendered Dataset](https://mlsanity.com/paper/2303.11011/) (2023)
- [Motion-prior Contrast Maximization for Dense Continuous-Time Motion Estimation](https://mlsanity.com/paper/2407.10802/) (2024)
- [FLSea: Underwater Visual-Inertial and Stereo-Vision Forward-Looking Datasets](https://mlsanity.com/paper/2302.12772/) (2023)
- [UNav-Sim: A Visually Realistic Underwater Robotics Simulator and Synthetic Data-generation Framework](https://mlsanity.com/paper/2310.11927/) (2023)
- [DAVE Aquatic Virtual Environment: Toward a General Underwater Robotics Simulator](https://mlsanity.com/paper/2209.02862/) (2022)
- [Stonefish: Supporting Machine Learning Research in Marine Robotics](https://mlsanity.com/paper/2502.11887/) (2025)
- [OysterSim: Underwater Simulation for Enhancing Oyster Reef Monitoring](https://mlsanity.com/paper/2209.09395/) (2022)
- [PHISWID: Physics-Inspired Underwater Image Dataset Synthesized from RGB-D Images](https://mlsanity.com/paper/2404.03998/) (2024)
- [Secrets of Edge-Informed Contrast Maximization for Event-Based Vision](https://mlsanity.com/paper/2409.14611/) (2024)
- [AquaticVision: Benchmarking Visual SLAM in Underwater Environment with Events and Frames](https://mlsanity.com/paper/2505.03448/) (2025)
- [Neuromorphic Perception and Navigation for Mobile Robots: A Review](https://mlsanity.com/paper/2407.06792/) (2024)
- [Optical Ocean Recipes: Creating Realistic Datasets to Facilitate Underwater Vision Research](https://mlsanity.com/paper/2509.20171/) (2025)
- [OceanLens: An Adaptive Backscatter and Edge Correction using Deep Learning Model for Enhanced Underwater Imaging](https://mlsanity.com/paper/2411.13230/) (2024)
- [eCARLA-scenes: A synthetically generated dataset for event-based optical flow prediction](https://mlsanity.com/paper/2412.09209/) (2024)
- [EREBUS: End-to-end Robust Event Based Underwater Simulation](https://mlsanity.com/paper/2511.01381/) (2025)
- [EvtSlowTV -- A Large and Diverse Dataset for Event-Based Depth Estimation](https://mlsanity.com/paper/2511.02953/) (2025)
- [Inertia-Informed Orientation Priors for Event-Based Optical Flow Estimation](https://mlsanity.com/paper/2511.12961/) (2025)

## Abstract (arXiv)

Underwater imaging is fundamentally challenging due to wavelength-dependent light attenuation, strong scattering from suspended particles, turbidity-induced blur, and non-uniform 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 benchmark dataset for event-based optical flow 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. Our dataset establishes a new baseline for future development and evaluation of underwater event-based perception algorithms. The source code and dataset for this project are publicly available at https://robotic-vision-lab.github.io/ueof.