  Cross-resolution image alignment is a key problem in multiscale gigapixel
photography, which requires to estimate homography matrix using images with
large resolution gap. Existing deep homography methods concatenate the input
images or features, neglecting the explicit formulation of correspondences
between them, which leads to degraded accuracy in cross-resolution challenges.
In this paper, we consider the cross-resolution homography estimation as a
multimodal problem, and propose a local transformer network embedded within a
multiscale structure to explicitly learn correspondences between the multimodal
inputs, namely, input images with different resolutions. The proposed local
transformer adopts a local attention map specifically for each position in the
feature. By combining the local transformer with the multiscale structure, the
network is able to capture long-short range correspondences efficiently and
accurately. Experiments on both the MS-COCO dataset and the real-captured
cross-resolution dataset show that the proposed network outperforms existing
state-of-the-art feature-based and deep-learning-based homography estimation
methods, and is able to accurately align images under $10\times$ resolution
gap.
