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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 51–100 of 134 papers

TitleStatusHype
UASTHN: Uncertainty-Aware Deep Homography Estimation for UAV Satellite-Thermal Geo-localizationCode1
Road-aware Monocular Structure from Motion and Homography Estimation—0
Robust Homography Estimation via Dual Principal Component Pursuit—0
Robust Multiple Homography Estimation: An Ill-Solved Problem—0
Scene-Aware Feature Matching—0
SeFENet: Robust Deep Homography Estimation via Semantic-Driven Feature Enhancement—0
STN-Homography: estimate homography parameters directly—0
AbHE: All Attention-based Homography Estimation—0
AffineGlue: Joint Matching and Robust Estimation—0
A Large-Scale Homography Benchmark—0
ViewpointDepth: A New Dataset for Monocular Depth Estimation Under Viewpoint Shifts—0
Are Semi-Dense Detector-Free Methods Good at Matching Local Features?—0
A Robust and Efficient Framework for Sports-Field Registration—0
A robust and efficient video representation for action recognition—0
Automated Camera Calibration via Homography Estimation with GNNs—0
Automated Top View Registration of Broadcast Football Videos—0
Camera Intrinsic Blur Kernel Estimation: A Reliable Framework—0
Character Keypoint-based Homography Estimation in Scanned Documents for Efficient Information Extraction—0
CLKN: Cascaded Lucas-Kanade Networks for Image Alignment—0
Coding local and global binary visual features extracted from video sequences—0
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
Full explicit consistency constraints in uncalibrated multiple homography estimation—0
G2MF-WA: Geometric Multi-Model Fitting with Weakly Annotated Data—0
Galois/monodromy groups for decomposing minimal problems in 3D reconstruction—0
Generative Adversarial Frontal View to Bird View Synthesis—0
Geometric Multi-Model Fitting with a Convex Relaxation Algorithm—0
GPO: Global Plane Optimization for Fast and Accurate Monocular SLAM Initialization—0
GyroFlow+: Gyroscope-Guided Unsupervised Deep Homography and Optical Flow Learning—0
HomographyAD: Deep Anomaly Detection Using Self Homography Learning—0
Homography Estimation From the Common Self-Polar Triangle of Separate Ellipses—0
Homography Estimation in Complex Topological Scenes—0
Homography Estimation with Convolutional Neural Networks Under Conditions of Variance—0
HomoMatcher: Dense Feature Matching Results with Semi-Dense Efficiency by Homography Estimation—0
HSolo: Homography from a single affine aware correspondence—0
Insights into the robustness of control point configurations for homography and planar pose estimation—0
Inverting RANSAC: Global Model Detection via Inlier Rate Estimation—0
Localization of Ice-Rink for Broadcast Hockey Videos—0
LocalTrans: A Multiscale Local Transformer Network for Cross-Resolution Homography Estimation—0
Medical Image Analysis using Deep Relational Learning—0
Noisy One-point Homographies are Surprisingly Good—0
Nonlinear constructive observer design for direct homography estimation—0
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