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Homography Estimation

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.

Papers

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Showing 71–80 of 134 papers

TitleStatusHype
Co-Attention for Conditioned Image Matching—0
Decoupled Geometric Parameterization and its Application in Deep Homography Estimation—0
Deep Exposure Fusion with Deghosting via Homography Estimation and Attention Learning—0
Deep Learning Reforms Image Matching: A Survey and Outlook—0
DeepMeshFlow: Content Adaptive Mesh Deformation for Robust Image Registration—0
Direct Structure Estimation for 3D Reconstruction—0
Explicit homography estimation improves contrastive self-supervised learning—0
Feature-based Recursive Observer Design for Homography Estimation—0
FisheyeSuperPoint: Keypoint Detection and Description Network for Fisheye Images—0
FMRT: Learning Accurate Feature Matching with Reconciliatory Transformer—0
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