Homography Estimation

50 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.

Most implemented papers

SuperPoint: Self-Supervised Interest Point Detection and Description

magicleap/SuperPointPretrainedNetwork 20 Dec 2017

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.

Deep Image Homography Estimation

JirongZhang/DeepHomography 13 Jun 2016

We present a deep convolutional neural network for estimating the relative homography between a pair of images.

Unsupervised Deep Homography: A Fast and Robust Homography Estimation Model

tynguyen/unsupervisedDeepHomographyRAL2018 12 Sep 2017

Homography estimation between multiple aerial images can provide relative pose estimation for collaborative autonomous exploration and monitoring.

CONSAC: Robust Multi-Model Fitting by Conditional Sample Consensus

fkluger/consac CVPR 2020

We present a robust estimator for fitting multiple parametric models of the same form to noisy measurements.

MAGSAC: marginalizing sample consensus

danini/magsac CVPR 2019

A method called, sigma-consensus, is proposed to eliminate the need for a user-defined inlier-outlier threshold in RANSAC.

UnsuperPoint: End-to-end Unsupervised Interest Point Detector and Descriptor

791136190/UnsuperPoint_PyTorch 9 Jul 2019

In this work, we introduce an unsupervised deep learning-based interest point detector and descriptor.

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.

DUT: Learning Video Stabilization by Simply Watching Unstable Videos

Annbless/DUTCode 30 Nov 2020

In this paper, we attempt to tackle the video stabilization problem in a deep unsupervised learning manner, which borrows the divide-and-conquer idea from traditional stabilizers while leveraging the representation power of DNNs to handle the challenges in real-world scenarios.

ALIKE: Accurate and Lightweight Keypoint Detection and Descriptor Extraction

Shiaoming/ALIKE 6 Dec 2021

The reprojection loss is then proposed to directly optimize these sub-pixel keypoints, and the dispersity peak loss is presented for accurate keypoints regularization.

A Large Scale Homography Benchmark

danini/homography-benchmark 20 Feb 2023

We present a large-scale dataset of Planes in 3D, Pi3D, of roughly 1000 planes observed in 10 000 images from the 1DSfM dataset, and HEB, a large-scale homography estimation benchmark leveraging Pi3D.