  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 paper 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 is
conceptually novel as it does not require explicit regularization, smoothing
(like median filtering) or a new data term, but solely our novel purely data
based search strategy that finds most inliers (even for small objects), while
it effectively avoids finding outliers. Moreover, we present novel enhancements
for outlier filtering. We show that our approach is better suited for large
displacement optical flow estimation than state-of-the-art descriptor matching
techniques. We do so by initializing EpicFlow (so far the best method on
MPI-Sintel) with our Flow Fields instead of their originally used
state-of-the-art descriptor matching technique. We significantly outperform the
original EpicFlow on MPI-Sintel, KITTI and Middlebury.
