Title: A Unifying Contrast Maximization Framework for Event Cameras, with Applications to Motion, Depth, and Optical Flow Estimation,
Abstract: We present a unifying framework to solve several computer vision problems
with event cameras: motion, depth and optical flow estimation. The main idea of
our framework is to find the point trajectories on the image plane that are
best aligned with the event data by maximizing an objective function: the
contrast of an image of warped events. Our method implicitly handles data
association between the events, and therefore, does not rely on additional
appearance information about the scene. In addition to accurately recovering
the motion parameters of the problem, our framework produces motion-corrected
edge-like images with high dynamic range that can be used for further scene
analysis. The proposed method is not only simple, but more importantly, it is,
to the best of our knowledge, the first method that can be successfully applied
to such a diverse set of important vision tasks with event cameras.