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
this http URL

本文提出了一种简单的无监督方法来完成不同任务下两张图像之间的密集对齐问题，采用参数化和非参数化的两个阶段方法来实现初步和精细对齐，其中初步对齐使用了 RANSAC 和现成的深度特征，精细对齐使用了一种优化标准结构相似度度量的深度网络，并且已被应用于多个领域的数据集，包括 KITTI、Hpatches、YFCC100M、Aachen Day-Night 和 Brughel dataset。