  As a bio-inspired sensor with high temporal resolution, the spiking camera
has an enormous potential in real applications, especially for motion
estimation in high-speed scenes. However, frame-based and event-based methods
are not well suited to spike streams from the spiking camera due to the
different data modalities. To this end, we present, SCFlow, a tailored deep
learning pipeline to estimate optical flow in high-speed scenes from spike
streams. Importantly, a novel input representation is introduced which can
adaptively remove the motion blur in spike streams according to the prior
motion. Further, for training SCFlow, we synthesize two sets of optical flow
data for the spiking camera, SPIkingly Flying Things and Photo-realistic
High-speed Motion, denoted as SPIFT and PHM respectively, corresponding to
random high-speed and well-designed scenes. Experimental results show that the
SCFlow can predict optical flow from spike streams in different high-speed
scenes. Moreover, SCFlow shows promising generalization on \textbf{real spike
streams}. Codes and datasets refer to
https://github.com/Acnext/Optical-Flow-For-Spiking-Camera.
