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
Most implemented papers
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.
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.
