  Event-based cameras display great potential for a variety of tasks such as
high-speed motion detection and navigation in low-light environments where
conventional frame-based cameras suffer critically. This is attributed to their
high temporal resolution, high dynamic range, and low-power consumption.
However, conventional computer vision methods as well as deep Analog Neural
Networks (ANNs) are not suited to work well with the asynchronous and discrete
nature of event camera outputs. Spiking Neural Networks (SNNs) serve as ideal
paradigms to handle event camera outputs, but deep SNNs suffer in terms of
performance due to the spike vanishing phenomenon. To overcome these issues, we
present Spike-FlowNet, a deep hybrid neural network architecture integrating
SNNs and ANNs for efficiently estimating optical flow from sparse event camera
outputs without sacrificing the performance. The network is end-to-end trained
with self-supervised learning on Multi-Vehicle Stereo Event Camera (MVSEC)
dataset. Spike-FlowNet outperforms its corresponding ANN-based method in terms
of the optical flow prediction capability while providing significant
computational efficiency.
