  Event camera has offered promising alternative for visual perception,
especially in high speed and high dynamic range scenes. Recently, many deep
learning methods have shown great success in providing promising solutions to
many event-based problems, such as optical flow estimation. However, existing
deep learning methods did not address the importance of temporal information
well from the perspective of architecture design and cannot effectively extract
spatio-temporal features. Another line of research that utilizes Spiking Neural
Network suffers from training issues for deeper architecture.To address these
points, a novel input representation is proposed that captures the events'
temporal distribution for signal enhancement. Moreover, we introduce a
spatio-temporal recurrent encoding-decoding neural network architecture for
event-based optical flow estimation, which utilizes Convolutional Gated
Recurrent Units to extract feature maps from a series of event images. Besides,
our architecture allows some traditional frame-based core modules, such as
correlation layer and iterative residual refine scheme, to be incorporated. The
network is end-to-end trained with self-supervised learning on the
Multi-Vehicle Stereo Event Camera dataset. We have shown that it outperforms
all the existing state-of-the-art methods by a large margin. The code link is
https://github.com/ruizhao26/STE-FlowNet.
