  Event-based cameras have shown great promise in a variety of situations where
frame based cameras suffer, such as high speed motions and high dynamic range
scenes. However, developing algorithms for event measurements requires a new
class of hand crafted algorithms. Deep learning has shown great success in
providing model free solutions to many problems in the vision community, but
existing networks have been developed with frame based images in mind, and
there does not exist the wealth of labeled data for events as there does for
images for supervised training. To these points, we present EV-FlowNet, a novel
self-supervised deep learning pipeline for optical flow estimation for event
based cameras. In particular, we introduce an image based representation of a
given event stream, which is fed into a self-supervised neural network as the
sole input. The corresponding grayscale images captured from the same camera at
the same time as the events are then used as a supervisory signal to provide a
loss function at training time, given the estimated flow from the network. We
show that the resulting network is able to accurately predict optical flow from
events only in a variety of different scenes, with performance competitive to
image based networks. This method not only allows for accurate estimation of
dense optical flow, but also provides a framework for the transfer of other
self-supervised methods to the event-based domain.
