  Homography estimation is a basic image alignment method in many applications.
It is usually conducted by extracting and matching sparse feature points, which
are error-prone in low-light and low-texture images. On the other hand,
previous deep homography approaches use either synthetic images for supervised
learning or aerial images for unsupervised learning, both ignoring the
importance of handling depth disparities and moving objects in real world
applications. To overcome these problems, in this work we propose an
unsupervised deep homography method with a new architecture design. In the
spirit of the RANSAC procedure in traditional methods, we specifically learn an
outlier mask to only select reliable regions for homography estimation. We
calculate loss with respect to our learned deep features instead of directly
comparing image content as did previously. To achieve the unsupervised
training, we also formulate a novel triplet loss customized for our network. We
verify our method by conducting comprehensive comparisons on a new dataset that
covers a wide range of scenes with varying degrees of difficulties for the
task. Experimental results reveal that our method outperforms the
state-of-the-art including deep solutions and feature-based solutions.
