Homography estimation is erroneous in the case of large-baseline due to the
low image overlay and limited receptive field. To address it, we propose a
progressive estimation strategy by converting large-baseline homography into
multiple intermediate ones, cumulatively multiplying these intermediate items
can reconstruct the initial homography. Meanwhile, a semi-supervised homography
identity loss, which consists of two components: a supervised objective and an
unsupervised objective, is introduced. The first supervised loss is acting to
optimize intermediate homographies, while the second unsupervised one helps to
estimate a large-baseline homography without photometric losses. To validate
our method, we propose a large-scale dataset that covers regular and
challenging scenes. Experiments show that our method achieves state-of-the-art
performance in large-baseline scenes while keeping competitive performance in
small-baseline scenes. Code and dataset are available at
this https URL

本文提出了一种基于渐进式估计策略和半监督学习的方法来解决大基线情况下的单应矩阵估计问题，该方法在大基线场景下取得了最先进的性能，并在小基线场景中保持了竞争性能。