  In this work, we propose a novel event based stereo method which addresses
the problem of motion blur for a moving event camera. Our method uses the
velocity of the camera and a range of disparities to synchronize the positions
of the events, as if they were captured at a single point in time. We represent
these events using a pair of novel time synchronized event disparity volumes,
which we show remove motion blur for pixels at the correct disparity in the
volume, while further blurring pixels at the wrong disparity. We then apply a
novel matching cost over these time synchronized event disparity volumes, which
both rewards similarity between the volumes while penalizing blurriness. We
show that our method outperforms more expensive, smoothing based event stereo
methods, by evaluating on the Multi Vehicle Stereo Event Camera dataset.
