  Event cameras are novel sensors that report brightness changes in the form of
asynchronous "events" instead of intensity frames. They have significant
advantages over conventional cameras: high temporal resolution, high dynamic
range, and no motion blur. Since the output of event cameras is fundamentally
different from conventional cameras, it is commonly accepted that they require
the development of specialized algorithms to accommodate the particular nature
of events. In this work, we take a different view and propose to apply
existing, mature computer vision techniques to videos reconstructed from event
data. We propose a novel recurrent network to reconstruct videos from a stream
of events, and train it on a large amount of simulated event data. Our
experiments show that our approach surpasses state-of-the-art reconstruction
methods by a large margin (> 20%) in terms of image quality. We further apply
off-the-shelf computer vision algorithms to videos reconstructed from event
data on tasks such as object classification and visual-inertial odometry, and
show that this strategy consistently outperforms algorithms that were
specifically designed for event data. We believe that our approach opens the
door to bringing the outstanding properties of event cameras to an entirely new
range of tasks. A video of the experiments is available at
https://youtu.be/IdYrC4cUO0I
