  Modern large displacement optical flow algorithms usually use an
initialization by either sparse descriptor matching techniques or dense
approximate nearest neighbor fields. While the latter have the advantage of
being dense, they have the major disadvantage of being very outlier-prone as
they are not designed to find the optical flow, but the visually most similar
correspondence. In this article we present a dense correspondence field
approach that is much less outlier-prone and thus much better suited for
optical flow estimation than approximate nearest neighbor fields. Our approach
does not require explicit regularization, smoothing (like median filtering) or
a new data term. Instead we solely rely on patch matching techniques and a
novel multi-scale matching strategy. We also present enhancements for outlier
filtering. We show that our approach is better suited for large displacement
optical flow estimation than modern descriptor matching techniques. We do so by
initializing EpicFlow with our approach instead of their originally used
state-of-the-art descriptor matching technique. We significantly outperform the
original EpicFlow on MPI-Sintel, KITTI 2012, KITTI 2015 and Middlebury. In this
extended article of our former conference publication we further improve our
approach in matching accuracy as well as runtime and present more experiments
and insights.
