Homography Estimation
53 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
Deep Homography Estimation for Visual Place Recognition
lu-feng/dhe-vpr
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Moreover, we design a re-projection error of inliers loss to train the DHE network without additional homography labels, which can also be jointly trained with the backbone network to help it extract the features that are more suitable for local matching.
Fine-Grained Cross-View Geo-Localization Using a Correlation-Aware Homography Estimator
xlwangdev/hc-net
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NeurIPS 2023
In this paper, we introduce a novel approach to fine-grained cross-view geo-localization.
LightGlue: Local Feature Matching at Light Speed
We introduce LightGlue, a deep neural network that learns to match local features across images.
SIDAR: Synthetic Image Dataset for Alignment & Restoration
Our data generation pipeline is customizable and can be applied to any existing dataset, serving as a data augmentation to further improve the feature learning of any existing method.
Attention Weighted Local Descriptors
vignywang/AWDesc
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journal 2023
Local features detection and description are widely used in many vision applications with high industrial and commercial demands.
Analyzing the Domain Shift Immunity of Deep Homography Estimation
MingzhenShao/Homography_estimation
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This study explores the resilience of a variety of deep homography estimation models to domain shifts, revealing that the network architecture itself is not a contributing factor to this remarkable adaptability.
SiLK -- Simple Learned Keypoints
Keypoint detection & descriptors are foundational tech-nologies for computer vision tasks like image matching, 3D reconstruction and visual odometry.
ALIKED: A Lighter Keypoint and Descriptor Extraction Network via Deformable Transformation
Shiaoming/ALIKE
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Image keypoints and descriptors play a crucial role in many visual measurement tasks.
Learning Knowledge-Rich Sequential Model for Planar Homography Estimation in Aerial Video
paul-lipu/deepvideohomography
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To address this concern, we develop a sequential estimator that directly processes a sequence of video frames and estimates their pairwise planar homographic transformations in batches.
A Large Scale Homography Benchmark
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.
