  Estimating homography from an image pair is a fundamental problem in image
alignment. Unsupervised learning methods have received increasing attention in
this field due to their promising performance and label-free training. However,
existing methods do not explicitly consider the problem of plane-induced
parallax, which will make the predicted homography compromised on multiple
planes. In this work, we propose a novel method HomoGAN to guide unsupervised
homography estimation to focus on the dominant plane. First, a multi-scale
transformer network is designed to predict homography from the feature pyramids
of input images in a coarse-to-fine fashion. Moreover, we propose an
unsupervised GAN to impose coplanarity constraint on the predicted homography,
which is realized by using a generator to predict a mask of aligned regions,
and then a discriminator to check if two masked feature maps are induced by a
single homography. To validate the effectiveness of HomoGAN and its components,
we conduct extensive experiments on a large-scale dataset, and the results show
that our matching error is 22% lower than the previous SOTA method. Code is
available at https://github.com/megvii-research/HomoGAN.
