Aquatic Neuromorphic Optical Flow

TL;DR

This paper introduces a neuromorphic, self-supervised optical flow estimation method for underwater environments using event cameras, enabling efficient, real-time perception on resource-limited underwater platforms.

Contribution

It pioneers the use of spiking neural networks for underwater optical flow estimation, addressing data scarcity and computational efficiency challenges.

Findings

Achieves competitive accuracy compared to state-of-the-art methods.

Operates with higher computational efficiency.

Demonstrates effectiveness in resource-constrained underwater scenarios.

Abstract

Underwater environments impose severe constraints on conventional imaging systems and demand solutions that balance high-quality sensing with strict resource efficiency. While emerging event cameras offer a promising alternative, their potential in aquatic scenarios remains largely unexplored. Through the lens of neuromorphic vision, this work pioneers the investigation of motion fields that serve as key media for agile underwater perception. Built upon spiking neural networks, we introduce a self-supervised framework to estimate per-pixel optical flow from asynchronous event streams, elegantly bypassing the long-standing bottleneck of underwater data scarcity. Extensive evaluations demonstrate that our method achieves competitive visual and quantitative results against leading techniques while operating with superior computational efficiency. By bridging neuromorphic sensing and aquatic intelligence, this work opens new frontiers for lightweight, real-time, and low-cost perception on resource-constrained underwater edge platforms.