  This paper considers the generic problem of dense alignment between two
images, whether they be two frames of a video, two widely different views of a
scene, two paintings depicting similar content, etc. Whereas each such task is
typically addressed with a domain-specific solution, we show that a simple
unsupervised approach performs surprisingly well across a range of tasks. Our
main insight is that parametric and non-parametric alignment methods have
complementary strengths. We propose a two-stage process: first, a feature-based
parametric coarse alignment using one or more homographies, followed by
non-parametric fine pixel-wise alignment. Coarse alignment is performed using
RANSAC on off-the-shelf deep features. Fine alignment is learned in an
unsupervised way by a deep network which optimizes a standard structural
similarity metric (SSIM) between the two images, plus cycle-consistency.
Despite its simplicity, our method shows competitive results on a range of
tasks and datasets, including unsupervised optical flow on KITTI, dense
correspondences on Hpatches, two-view geometry estimation on YFCC100M,
localization on Aachen Day-Night, and, for the first time, fine alignment of
artworks on the Brughel dataset. Our code and data are available at
http://imagine.enpc.fr/~shenx/RANSAC-Flow/
