computer-vision-5
Homography Estimation
3260 papers • 126 benchmarks • 313 datasets
Homography estimation is a technique used in computer vision and image processing to find the relationship between two images of the same scene, but captured from different viewpoints. It is used to align images, correct for perspective distortions, or perform image stitching. In order to estimate the homography, a set of corresponding points between the two images must be found, and a mathematical model must be fit to these points. There are various algorithms and techniques that can be used to perform homography estimation, including direct methods, RANSAC, and machine learning-based approaches.
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S-COCO
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Oxford and Paris
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Most implemented papers
SuperPoint: Self-Supervised Interest Point Detection and Description
Daniel DeTone, Tomasz Malisiewicz, Andrew Rabinovich•Tue Dec 19 2017
This paper presents a self-supervised framework for training interest point detectors and descriptors suitable for a large number of multiple-view geometry problems in computer vision and introduces Homographic Adaptation, a multi-scale, multi-homography approach for boosting interest point detection repeatability and performing cross-domain adaptation.
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