  We propose Iterative Homography Network, namely IHN, a new deep homography
estimation architecture. Different from previous works that achieve iterative
refinement by network cascading or untrainable IC-LK iterator, the iterator of
IHN has tied weights and is completely trainable. IHN achieves state-of-the-art
accuracy on several datasets including challenging scenes. We propose 2
versions of IHN: (1) IHN for static scenes, (2) IHN-mov for dynamic scenes with
moving objects. Both versions can be arranged in 1-scale for efficiency or
2-scale for accuracy. We show that the basic 1-scale IHN already outperforms
most of the existing methods. On a variety of datasets, the 2-scale IHN
outperforms all competitors by a large gap. We introduce IHN-mov by producing
an inlier mask to further improve the estimation accuracy of moving-objects
scenes. We experimentally show that the iterative framework of IHN can achieve
95% error reduction while considerably saving network parameters. When
processing sequential image pairs, IHN can achieve 32.7 fps, which is about 8x
the speed of IC-LK iterator. Source code is available at
https://github.com/imdumpl78/IHN.
