Homography Estimation
52 papers with code • 4 benchmarks • 7 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.
MS COCO
S-COCO
YUD+
POT-210
NYU-VP
PDS-COCO
VidSet
Latest papers with no code
No Bells, Just Whistles: Sports Field Registration by Leveraging Geometric Properties
Broadcast sports field registration is traditionally addressed as a homography estimation task, mapping the visible image area to a planar field model, predominantly focusing on the main camera shot.
View-Centric Multi-Object Tracking with Homographic Matching in Moving UAV
In this paper, we address the challenge of multi-object tracking (MOT) in moving Unmanned Aerial Vehicle (UAV) scenarios, where irregular flight trajectories, such as hovering, turning left/right, and moving up/down, lead to significantly greater complexity compared to fixed-camera MOT.
NeRF-Supervised Feature Point Detection and Description
Feature point detection and description is the backbone for various computer vision applications, such as Structure-from-Motion, visual SLAM, and visual place recognition.
Are Semi-Dense Detector-Free Methods Good at Matching Local Features?
We then propose to limit the computation of the matching accuracy to textured regions, and show that in this case SAM often surpasses SDF methods.
Video-based Sequential Bayesian Homography Estimation for Soccer Field Registration
A novel Bayesian framework is proposed, which explicitly relates the homography of one video frame to the next through an affine transformation while explicitly modelling keypoint uncertainty.
Automated Camera Calibration via Homography Estimation with GNNs
We propose a framework involving the generation of a set of synthetic intersection viewpoint images from a bird's-eye-view image, framed as a graph of virtual cameras to model these images.
FMRT: Learning Accurate Feature Matching with Reconciliatory Transformer
However, these methods only integrate long-range context information among keypoints with a fixed receptive field, which constrains the network from reconciling the importance of features with different receptive fields to realize complete image perception, hence limiting the matching accuracy.
Scene-Aware Feature Matching
Current feature matching methods focus on point-level matching, pursuing better representation learning of individual features, but lacking further understanding of the scene.
Homography Estimation in Complex Topological Scenes
Surveillance videos and images are used for a broad set of applications, ranging from traffic analysis to crime detection.
AffineGlue: Joint Matching and Robust Estimation
Moreover, we derive a new minimal solver for homography estimation, requiring only a single affine correspondence (AC) and a gravity prior.
