Homography Estimation
Leaderboards
| TREND | DATASET | BEST METHOD | PAPER TITLE | PAPER | CODE | COMPARE |
|---|---|---|---|---|---|---|
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Greatest papers with code
SuperPoint: Self-Supervised Interest Point Detection and Description
• magicleap/SuperPointPretrainedNetwork
• 
This paper presents a self-supervised framework for training interest point detectors and descriptors suitable for a large number of multiple-view geometry problems in computer vision.
Unsupervised Deep Homography: A Fast and Robust Homography Estimation Model
• tynguyen/unsupervisedDeepHomographyRAL2018
• 
Homography estimation between multiple aerial images can provide relative pose estimation for collaborative autonomous exploration and monitoring.
MAGSAC: marginalizing sample consensus
A method called, sigma-consensus, is proposed to eliminate the need for a user-defined inlier-outlier threshold in RANSAC.
Content-Aware Unsupervised Deep Homography Estimation
However, the quality of homography heavily relies on the quality of image features, which are prone to errors with respect to low light and low texture images.
Deep Image Homography Estimation
We present a deep convolutional neural network for estimating the relative homography between a pair of images.
STag: A Stable Fiducial Marker System
Jitter impairs robustness in vision and robotics applications, and deteriorates the sense of presence and immersion in AR/VR applications.
Neural Outlier Rejection for Self-Supervised Keypoint Learning
By making the sampling of inlier-outlier sets from point-pair correspondences fully differentiable within the keypoint learning framework, we show that are able to simultaneously self-supervise keypoint description and improve keypoint matching.
Latent RANSAC
We present a method that can evaluate a RANSAC hypothesis in constant time, i. e. independent of the size of the data.
Homography from two orientation- and scale-covariant features
Two new general constraints are derived on the scales and rotations which can be used in any geometric model estimation tasks.
Rethinking Planar Homography Estimation Using Perspective Fields
ACCV 2018 2019
• ruizengalways/PFNet
•
The PF is naturally learned by the proposed fully convolutional residual network, PFNet, to keep the spatial order of each pixel.
SOTA for Homography Estimation on COCO 2014