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
this https URL

介绍了一种基于深度学习的用于估计高速场景下 spiking 相机的光流的流水线，并引入了一种新的输入表示方法以自适应地去除 spike 流中的运动模糊。通过在名为 SPIFT 和 PHM 的场景中合成的光流数据进行训练，该方法显示出对不同高速场景下的 spiking 数据能够有效地预测结果。