RANSAC-Flow: generic two-stage image alignment

• URL: http://arxiv.org/abs/2004.01526v2
• Date: Fri, 17 Jul 2020 13:51:18 GMT
• Title: RANSAC-Flow: generic two-stage image alignment
• Authors: Xi Shen, Fran\c{c}ois Darmon, Alexei A. Efros, Mathieu Aubry
• Abstract summary: We show that a simple unsupervised approach performs surprisingly well across a range of tasks. Despite its simplicity, our method shows competitive results on a range of tasks and datasets.
• Score: 53.11926395028508
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper considers the generic problem of dense alignment between two images, whether they be two frames of a video, two widely different views of a scene, two paintings depicting similar content, etc. Whereas each such task is typically addressed with a domain-specific solution, we show that a simple unsupervised approach performs surprisingly well across a range of tasks. Our main insight is that parametric and non-parametric alignment methods have complementary strengths. We propose a two-stage process: first, a feature-based parametric coarse alignment using one or more homographies, followed by non-parametric fine pixel-wise alignment. Coarse alignment is performed using RANSAC on off-the-shelf deep features. Fine alignment is learned in an unsupervised way by a deep network which optimizes a standard structural similarity metric (SSIM) between the two images, plus cycle-consistency. Despite its simplicity, our method shows competitive results on a range of tasks and datasets, including unsupervised optical flow on KITTI, dense correspondences on Hpatches, two-view geometry estimation on YFCC100M, localization on Aachen Day-Night, and, for the first time, fine alignment of artworks on the Brughel dataset. Our code and data are available at http://imagine.enpc.fr/~shenx/RANSAC-Flow/

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