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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
UASTHN: Uncertainty-Aware Deep Homography Estimation for UAV Satellite-Thermal Geo-localizationCode1
Adapting Dense Matching for Homography Estimation with Grid-based AccelerationCode1
SSHNet: Unsupervised Cross-modal Homography Estimation via Problem Reformulation and Split OptimizationCode1
Unsupervised Homography Estimation on Multimodal Image Pair via Alternating OptimizationCode1
InterNet: Unsupervised Cross-modal Homography Estimation Based on Interleaved Modality Transfer and Self-supervised Homography PredictionCode1
SCPNet: Unsupervised Cross-modal Homography Estimation via Intra-modal Self-supervised LearningCode1
EarthMatch: Iterative Coregistration for Fine-grained Localization of Astronaut PhotographyCode1
MCNet: Rethinking the Core Ingredients for Accurate and Efficient Homography EstimationCode1
Fine-Grained Cross-View Geo-Localization Using a Correlation-Aware Homography EstimatorCode1
S2LD: Sparse-to-Local-Dense Matching for Geometry-Guided Correspondence EstimationCode1
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