Aquatic Neuromorphic Optical Flow

2026-05-08

AI Summary

This work addresses the challenges of data scarcity and limited computational resources in underwater optical flow estimation by introducing neuromorphic vision to this domain for the first time. The authors propose a self-supervised learning framework based on spiking neural networks that directly and efficiently estimates per-pixel optical flow from asynchronous event streams generated by event cameras. Requiring no labeled training data, the method significantly reduces computational overhead and power consumption while achieving visual quality and quantitative accuracy comparable to state-of-the-art approaches. This enables lightweight, real-time motion perception in underwater environments, making it well-suited for deployment on resource-constrained edge devices.

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.

Problem

• underwater optical flow
• event cameras
• neuromorphic vision
• resource-constrained perception
• motion estimation

Innovation

• neuromorphic vision
• event cameras
• spiking neural networks
• optical flow
• self-supervised learning