Homography Estimation
30 papers with code • 4 benchmarks • 7 datasets
COCO
S-COCO
YUD+
PDS-COCO
NYU-VP
POT-210
VidSet
Most implemented papers
SuperPoint: Self-Supervised Interest Point Detection and Description
magicleap/SuperGluePretrainedNetwork
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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
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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
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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
We present a robust estimator for fitting multiple parametric models of the same form to noisy measurements.
MAGSAC: marginalizing sample consensus
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
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In this work, we introduce an unsupervised deep learning-based interest point detector and descriptor.
Neural Outlier Rejection for Self-Supervised Keypoint Learning
TRI-ML/KP2D
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ICLR 2020
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.
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.
Latent RANSAC
We present a method that can evaluate a RANSAC hypothesis in constant time, i. e. independent of the size of the data.
Optimal Multi-view Correction of Local Affine Frames
The technique requires the epipolar geometry to be pre-estimated between each image pair.
