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- W4280574166 abstract "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 dominant plane. First, a multi-scale transformer network is designed to predict homography from the feature pyramids of input images in a coarse-to-fine fashion. Moreover, we propose an unsupervised GAN to impose coplanarity constraint on the predicted homography, which is realized by using a generator to predict a mask of aligned regions, and then a discriminator to check if two masked feature maps are induced by a single homography. To validate the effectiveness of HomoGAN and its components, we conduct extensive experiments on a large-scale dataset, and the results show that our matching error is 22% lower than the previous SOTA method. Code is available at https://github.com/megvii-research/HomoGAN." @default.
- W4280574166 created "2022-05-22" @default.
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- W4280574166 date "2022-05-08" @default.
- W4280574166 modified "2023-09-27" @default.
- W4280574166 title "Unsupervised Homography Estimation with Coplanarity-Aware GAN" @default.
- W4280574166 doi "https://doi.org/10.48550/arxiv.2205.03821" @default.
- W4280574166 hasPublicationYear "2022" @default.
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