Image Matching
12 papers with code • 1 benchmarks • 1 datasets
Image Matching or wide multiple baseline stereo (WxBS) is a process of establishing a sufficient number of pixel or region correspondences from two or more images depicting the same scene to estimate the geometric relationship between cameras, which produced these images.
Source: The Role of Wide Baseline Stereo in the Deep Learning World
( Image credit: Kornia )
Benchmarks
These leaderboards are used to track progress in Image Matching
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IMC PhotoTourism
Latest papers
Shared Coupling-bridge for Weakly Supervised Local Feature Learning
Sparse local feature extraction is usually believed to be of important significance in typical vision tasks such as simultaneous localization and mapping, image matching and 3D reconstruction.
Decoupling Makes Weakly Supervised Local Feature Better
The-Learning-And-Vision-Atelier-LAVA/PoSFeat
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CVPR 2022
Weakly supervised learning can help local feature methods to overcome the obstacle of acquiring a large-scale dataset with densely labeled correspondences.
LoFTR: Detector-Free Local Feature Matching with Transformers
We present a novel method for local image feature matching.
DISK: Learning local features with policy gradient
luigifreda/pyslam
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NeurIPS 2020
Local feature frameworks are difficult to learn in an end-to-end fashion, due to the discreteness inherent to the selection and matching of sparse keypoints.
R2D2: Reliable and Repeatable Detector and Descriptor
We thus propose to jointly learn keypoint detection and description together with a predictor of the local descriptor discriminativeness.
SuperGlue: Learning Feature Matching with Graph Neural Networks
magicleap/SuperGluePretrainedNetwork
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CVPR 2020
This paper introduces SuperGlue, a neural network that matches two sets of local features by jointly finding correspondences and rejecting non-matchable points.
Kornia: an Open Source Differentiable Computer Vision Library for PyTorch
kornia/kornia
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This work presents Kornia -- an open source computer vision library which consists of a set of differentiable routines and modules to solve generic computer vision problems.
D2-Net: A Trainable CNN for Joint Detection and Description of Local Features
In this work we address the problem of finding reliable pixel-level correspondences under difficult imaging conditions.
What Is Wrong With Scene Text Recognition Model Comparisons? Dataset and Model Analysis
clovaai/deep-text-recognition-benchmark
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ICCV 2019
Many new proposals for scene text recognition (STR) models have been introduced in recent years.
Key.Net: Keypoint Detection by Handcrafted and Learned CNN Filters
axelBarroso/Key.Net
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ICCV 2019
We introduce a novel approach for keypoint detection task that combines handcrafted and learned CNN filters within a shallow multi-scale architecture.
