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

2026-01-15

AI Summary

This work addresses the challenges of underwater imaging, where light attenuation, scattering, and uneven illumination severely degrade motion information captured by conventional cameras, and no existing optical flow dataset is tailored for event cameras in underwater environments. To bridge this gap, the authors present the first physically rendered synthetic benchmark dataset for underwater event-based optical flow. Leveraging ray tracing, they generate RGBD sequences that accurately simulate realistic underwater optical effects, which are then processed through a video-to-event conversion pipeline to produce synchronized event streams accompanied by ground-truth optical flow, depth, and camera pose. This dataset not only fills a critical void in underwater event-based perception but also establishes a standardized evaluation platform for existing optical flow algorithms in this domain.

Technology Category

• Computer Vision: Computational Photography, Image & Video Synthesis
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

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.

Problem

• underwater imaging
• event camera
• optical flow
• benchmark dataset
• light transport

Innovation

• event-based vision
• underwater optical flow
• synthetic benchmark dataset
• ray-traced rendering
• video-to-event conversion