# Aquatic Neuromorphic Optical Flow

arXiv: https://arxiv.org/abs/2605.07653 · HTML: https://mlsanity.com/paper/2605.07653/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 39 references that are in the map.
- First posted on arXiv 8 May 2026.

## Builds on (most cited first, 18 of 18)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [RAFT: Recurrent All-Pairs Field Transforms for Optical Flow](https://mlsanity.com/paper/2003.12039/) (2020)
- [Unsupervised Event-based Learning of Optical Flow, Depth, and Egomotion](https://mlsanity.com/paper/1812.08156/) (2018)
- [Spikformer: When Spiking Neural Network Meets Transformer](https://mlsanity.com/paper/2209.15425/) (2022)
- [EV-FlowNet: Self-Supervised Optical Flow Estimation for Event-based Cameras](https://mlsanity.com/paper/1802.06898/) (2018)
- [A Unifying Contrast Maximization Framework for Event Cameras, with Applications to Motion, Depth, and Optical Flow Estimation](https://mlsanity.com/paper/1804.01306/) (2018)
- [Delving Deeper into Convolutional Networks for Learning Video Representations](https://mlsanity.com/paper/1511.06432/) (2015)
- [Self-Supervised Learning of Event-Based Optical Flow with Spiking Neural Networks](https://mlsanity.com/paper/2106.01862/) (2021)
- [Focus Is All You Need: Loss Functions For Event-based Vision](https://mlsanity.com/paper/1904.07235/) (2019)
- [Taming Contrast Maximization for Learning Sequential, Low-latency, Event-based Optical Flow](https://mlsanity.com/paper/2303.05214/) (2023)
- [Adaptive-SpikeNet: Event-based Optical Flow Estimation using Spiking Neural Networks with Learnable Neuronal Dynamics](https://mlsanity.com/paper/2209.11741/) (2022)
- [Fast Event-based Optical Flow Estimation by Triplet Matching](https://mlsanity.com/paper/2212.12218/) (2022)
- [Neuromorphic Imaging with Joint Image Deblurring and Event Denoising](https://mlsanity.com/paper/2309.16106/) (2023)
- [SU-YOLO: Spiking Neural Network for Efficient Underwater Object Detection](https://mlsanity.com/paper/2503.24389/) (2025)
- [AquaticVision: Benchmarking Visual SLAM in Underwater Environment with Events and Frames](https://mlsanity.com/paper/2505.03448/) (2025)
- [Dark-EvGS: Event Camera as an Eye for Radiance Field in the Dark](https://mlsanity.com/paper/2507.11931/) (2025)
- [Neuromorphic Imaging with Super-Resolution](https://mlsanity.com/paper/2407.05764/) (2024)
- [Underwater Image Enhancement by Convolutional Spiking Neural Networks](https://mlsanity.com/paper/2503.20485/) (2025)

## Abstract (arXiv)

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.