  Event cameras offer promising properties, such as high temporal resolution
and high dynamic range. These benefits have been utilized into many machine
vision tasks, especially optical flow estimation. Currently, most existing
event-based works use deep learning to estimate optical flow. However, their
networks have not fully exploited prior hidden states and motion flows.
Additionally, their supervision strategy has not fully leveraged the geometric
constraints of event data to unlock the potential of networks. In this paper,
we propose EV-MGRFlowNet, an unsupervised event-based optical flow estimation
pipeline with motion-guided recurrent networks using a hybrid
motion-compensation loss. First, we propose a feature-enhanced recurrent
encoder network (FERE-Net) which fully utilizes prior hidden states to obtain
multi-level motion features. Then, we propose a flow-guided decoder network
(FGD-Net) to integrate prior motion flows. Finally, we design a hybrid
motion-compensation loss (HMC-Loss) to strengthen geometric constraints for the
more accurate alignment of events. Experimental results show that our method
outperforms the current state-of-the-art (SOTA) method on the MVSEC dataset,
with an average reduction of approximately 22.71% in average endpoint error
(AEE). To our knowledge, our method ranks first among unsupervised
learning-based methods.
