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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

Showing 1120 of 134 papers

TitleStatusHype
SeFENet: Robust Deep Homography Estimation via Semantic-Driven Feature Enhancement0
Unsupervised Homography Estimation on Multimodal Image Pair via Alternating OptimizationCode1
HomoMatcher: Dense Feature Matching Results with Semi-Dense Efficiency by Homography Estimation0
ViewpointDepth: A New Dataset for Monocular Depth Estimation Under Viewpoint Shifts0
InterNet: Unsupervised Cross-modal Homography Estimation Based on Interleaved Modality Transfer and Self-supervised Homography PredictionCode1
Semantic-aware Representation Learning for Homography EstimationCode0
SCPNet: Unsupervised Cross-modal Homography Estimation via Intra-modal Self-supervised LearningCode1
STHN: Deep Homography Estimation for UAV Thermal Geo-localization with Satellite ImageryCode2
TP3M: Transformer-based Pseudo 3D Image Matching with Reference Image0
EarthMatch: Iterative Coregistration for Fine-grained Localization of Astronaut PhotographyCode1
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