用事件相机实现水下实时运动感知，解决数据稀缺难题。
Aquatic Neuromorphic Optical Flow
- 基于脉冲神经网络，自监督估计事件流的逐像素光流。
- 在资源受限平台实现高效计算，性能媲美主流方法。
- 适合水下机器人、边缘感知等低功耗场景应用。
水下环境对传统成像系统构成严峻挑战，亟需在高质量感知与严格资源效率间取得平衡。尽管新兴事件相机提供了有前景的替代方案，其在水下场景的潜力仍鲜被探索。本文从神经形态视觉视角出发，首次系统研究了作为敏捷水下感知关键媒介的运动场。基于脉冲神经网络，提出一种自监督框架，从异步事件流中估计逐像素光流，巧妙绕过水下数据稀缺这一长期瓶颈。大量实验表明，该方法在视觉与定量指标上均达到领先水平，同时具备卓越的计算效率。本工作打通了神经形态感知与水下智能的壁垒，为资源受限的水下边缘平台开辟了轻量级、实时、低成本感知的新前沿。
原文摘要 · Abstract (English)
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.
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