  Event cameras such as DAVIS can simultaneously output high temporal
resolution events and low frame-rate intensity images, which own great
potential in capturing scene motion, such as optical flow estimation. Most of
the existing optical flow estimation methods are based on two consecutive image
frames and can only estimate discrete flow at a fixed time interval. Previous
work has shown that continuous flow estimation can be achieved by changing the
quantities or time intervals of events. However, they are difficult to estimate
reliable dense flow , especially in the regions without any triggered events.
In this paper, we propose a novel deep learning-based dense and continuous
optical flow estimation framework from a single image with event streams, which
facilitates the accurate perception of high-speed motion. Specifically, we
first propose an event-image fusion and correlation module to effectively
exploit the internal motion from two different modalities of data. Then we
propose an iterative update network structure with bidirectional training for
optical flow prediction. Therefore, our model can estimate reliable dense flow
as two-frame-based methods, as well as estimate temporal continuous flow as
event-based methods. Extensive experimental results on both synthetic and real
captured datasets demonstrate that our model outperforms existing event-based
state-of-the-art methods and our designed baselines for accurate dense and
continuous optical flow estimation.
