SSORN: Self-Supervised Outlier Removal Network for Robust Homography Estimation. (arXiv:2208.14093v1 [cs.CV])

The traditional homography estimation pipeline consists of four main steps:
feature detection, feature matching, outlier removal and transformation
estimation. Recent deep learning models intend to address the homography
estimation problem using a single convolutional network. While these models are
trained in an end-to-end fashion to simplify the homography estimation problem,
they lack the feature matching step and/or the outlier removal step, which are
important steps in the traditional homography estimation pipeline. In this
paper, we attempt to build a deep …

arxiv homography network