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Unsupervised Homography Estimation with Coplanarity-Aware GAN
[article]
2022
arXiv
pre-print
Estimating homography from an image pair is a fundamental problem in image alignment. Unsupervised learning methods have received increasing attention in this field due to their promising performance and label-free training. However, existing methods do not explicitly consider the problem of plane-induced parallax, which will make the predicted homography compromised on multiple planes. In this work, we propose a novel method HomoGAN to guide unsupervised homography estimation to focus on the
arXiv:2205.03821v1
fatcat:rvk6d6sgxnhvnpl6a77jsxgebq