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

TL;DR

This paper introduces UEOF, a synthetic underwater dataset for event-based optical flow, enabling research in challenging underwater imaging conditions with ground-truth data for motion and depth.

Contribution

The paper presents the first synthetic underwater dataset for event-based optical flow, combining realistic ray-traced sequences with benchmark evaluations of existing methods.

Findings

Event data streams closely mimic real underwater conditions.

State-of-the-art methods' performance is significantly affected by underwater light effects.

The dataset provides a new baseline for underwater event-based perception research.

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.