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.

本研究提出了一种新颖的事件驱动的立体视觉方法，解决了运动模糊问题。这种方法使用开发者提出的一些同步技巧，使用一对时间同步的事件视差体来表示事件，使用新的匹配成本来选择同步的事件视差体中的特征点，并在 Multi Vehicle Stereo Event Camera 数据集上表现出比传统方法更好的效果。