Aquatic Neuromorphic Optical Flow
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 demon...
| Authors | Pei Zhang, Yunkai Liang, Kaiqiang Wang |
|---|---|
| Primary category | cs.CV |
| Published | 8 May 2026 |
| Last updated at source | 13 May 2026 |
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