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
| Trend | Dataset | Best Model | Paper | Code | Compare |
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IMC PhotoTourism
Most implemented papers
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.
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.
LoFTR: Detector-Free Local Feature Matching with Transformers
We present a novel method for local image feature matching.
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.
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.
Repeatability Is Not Enough: Learning Affine Regions via Discriminability
ducha-aiki/affnet
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ECCV 2018
A method for learning local affine-covariant regions is presented.
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.
DISK: Learning local features with policy gradient
cvlab-epfl/disk
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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.
Three things everyone should know to improve object retrieval
The objective of this work is object retrieval in large scale image datasets, where the object is specified by an image query and retrieval should be immediate at run time in the manner of Video Google [28].
