Quantum mechanics

Projective test

Algorithm

Scale (ratio)

Projective space

Statistics

Set (abstract data type)

Computer science

Code (set theory)

Physics

Pattern recognition (psychology)

Mathematics

Artificial intelligence

Programming language

Homography

Iterative method

Computer vision

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 8× the speed of IC-LK iterator: Source code is available at https://github.com/imdump178/IHN.

Iterative Deep Homography Estimation