aquatic-neuromorphic-optical-flow

name: aquatic-neuromorphic-optical-flow description: "Self-supervised spiking neural network framework for estimating per-pixel optical flow from event camera streams in underwater environments. Bridges neuromorphic sensing and aquatic intelligence for lightweight, real-time, low-cost perception on resource-constrained edge platforms. Activation: underwater vision, event camera, neuromorphic optical flow, aquatic perception, spiking neural network motion estimation, DVS underwater." category: neuroscience

Aquatic Neuromorphic Optical Flow

Self-supervised SNN framework for per-pixel optical flow estimation from asynchronous event streams in underwater environments, bypassing the underwater data scarcity bottleneck.

Metadata

• Source: arXiv:2605.07653v1
• Authors: Pei Zhang, Yunkai Liang, Kaiqiang Wang
• Published: 2026-05-08
• Categories: cs.CV, eess.IV
• Status: Under review

Core Methodology

Problem

Underwater imaging faces severe constraints: light attenuation, scattering, turbidity, and strict resource limits on autonomous underwater vehicles (AUVs). Conventional frame-based cameras produce redundant data in aquatic environments where most of the scene is static between frames.

Key Innovation: Neuromorphic Vision for Aquatic Perception

• Event cameras (Dynamic Vision Sensors) report only pixel-level brightness changes (asynchronous events)
• Data bandwidth reduction: 10-100x compared to conventional RGB video
• High temporal resolution: microsecond-level event timestamps capture fast underwater motion
• High dynamic range: handles extreme lighting transitions underwater

Self-Supervised SNN Framework

1. Input: Asynchronous event streams from DVS cameras in underwater scenarios
2. Spiking Neural Network: Encodes event spatiotemporal patterns into spike trains
3. Motion field estimation: SNN learns to predict per-pixel optical flow without ground-truth labels
4. Self-supervision: Uses event warping consistency — predicted flow should align events to form a coherent image
5. Output: Dense optical flow field for downstream tasks (navigation, obstacle avoidance, object tracking)

Self-Supervision via Event Warping

• Predict flow field that "warps" events backward in time
• Minimize reconstruction error of warped events (events should align if flow is correct)
• No need for labeled optical flow data, bypassing the underwater annotation bottleneck

Implementation Guide

Prerequisites

• Event camera data (DVS) from underwater scenarios
• SNN training framework (SpikingJelly, Lava, or custom)
• GPU for training

Architecture

Events (x, y, t, polarity) 
  → Voxel grid representation (temporal binning)
  → SNN encoder (spiking conv layers)
  → Flow decoder
  → Optical flow field (u, v per pixel)
  → Event warping + reconstruction loss
  → Self-supervised training loop

Key Steps

1. Convert asynchronous events to voxel grid representation with temporal bins
2. Build SNN with leaky integrate-and-fire (LIF) neurons for temporal encoding
3. Train with event warping self-supervision loss
4. Evaluate optical flow quality via downstream task performance

Applications

• AUV navigation: Real-time motion perception for autonomous underwater vehicles
• Underwater obstacle avoidance: Low-latency collision detection
• Marine biology monitoring: Tracking aquatic organisms with minimal power
• Subsea inspection: Pipeline, cable, and structure monitoring
• Resource-constrained edge platforms: Deploy on battery-powered underwater sensors

Related Skills

• neuromorphic-spinnaker-asl
• snn-near-sensor-noise-filter-dvs
• direct-to-event-snn-transfer
• event-driven-neuromorphic-transceiver
• neuromorphic-spacecraft-pose-event-camera