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 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.

本文介绍了一种密集对应场方法，该方法具有非常低的异常值率，比近似最近邻字段更适合光流估计，并使用数据搜索策略取代了显式正则化和平滑(如中值过滤)或新的数据项。此外，我们提供了一种新颖的异常值过滤增强方法。我们显示了我们的方法比最先进的描述符匹配技术更适合大位移光流估计。通过使用我们的Flow Field来初始化EpicFlow，我们显着优于MPI-Sintel，KITTI和Middlebury上的原始EpicFlow。

Flow Fields:高精度大位移光流估计的密集对应场