Event cameras are bio-inspired vision sensors which
mimic retinas to measure per-pixel intensity change rather
than outputting an actual intensity image. This proposed
paradigm shift away from traditional frame cameras offers
significant potential advantages: namely avoiding high
data rates, dynamic range limitations and motion blur.
Unfortunately, however, established computer vision algorithms
may not at all be applied directly to event cameras.
Methods proposed so far to reconstruct images, estimate
optical flow, track a camera and reconstruct a scene come
with severe restrictions on the environment or on the motion
of the camera, e.g. allowing only rotation. Here, we
propose, to the best of our knowledge, the first algorithm to
simultaneously recover the motion field and brightness image,
while the camera undergoes a generic motion through
any scene. Our approach employs minimisation of a cost
function that contains the asynchronous event data as well
as spatial and temporal regularisation within a sliding window
time interval. Our implementation relies on GPU optimisation
and runs in near real-time. In a series of examples,
we demonstrate the successful operation of our framework,
including in situations where conventional cameras suffer
from dynamic range limitations and motion blur.