DeepMatcher: A Deep Transformer-based Network for Robust and Accurate Local Feature Matching [PDF]
Expert systems with applications, 2023 Local feature matching between images remains a challenging task, especially in the presence of significant appearance variations, e.g., extreme viewpoint changes.
Tao Xie +4 more
semanticscholar +1 more source
Real-World Blind Super-Resolution via Feature Matching with Implicit High-Resolution Priors [PDF]
ACM Multimedia, 2022 A key challenge of real-world image super-resolution (SR) is to recover the missing details in low-resolution (LR) images with complex unknown degradations (\eg, downsampling, noise and compression).
Chaofeng Chen +6 more
semanticscholar +1 more source
PATS: Patch Area Transportation with Subdivision for Local Feature Matching [PDF]
Computer Vision and Pattern Recognition, 2023 Local feature matching aims at establishing sparse correspondences between a pair of images. Recently, detector-free methods present generally better performance but are not satisfactory in image pairs with large scale differences.
Junjie Ni +6 more
semanticscholar +1 more source
Adaptive Spot-Guided Transformer for Consistent Local Feature Matching [PDF]
Computer Vision and Pattern Recognition, 2023 Local feature matching aims at finding correspondences between a pair of images. Although current detector-free methods leverage Transformer architecture to obtain an impressive performance, few works consider maintaining local consistency.
Jiahuan Yu +4 more
semanticscholar +1 more source
SuperGlue: Learning Feature Matching With Graph Neural Networks [PDF]
Computer Vision and Pattern Recognition, 2019 This paper introduces SuperGlue, a neural network that matches two sets of local features by jointly finding correspondences and rejecting non-matchable points.
Paul-Edouard Sarlin +3 more
semanticscholar +1 more source
Feature Matching Data Synthesis for Non-IID Federated Learning [PDF]
IEEE Transactions on Mobile Computing, 2023 Federated learning (FL) has emerged as a privacy-preserving paradigm that trains neural networks on edge devices without collecting data at a central server.
Zijian Li +5 more
semanticscholar +1 more source
DFNet: Enhance Absolute Pose Regression with Direct Feature Matching [PDF]
European Conference on Computer Vision, 2022 We introduce a camera relocalization pipeline that combines absolute pose regression (APR) and direct feature matching. By incorporating exposure-adaptive novel view synthesis, our method successfully addresses photometric distortions in outdoor ...
Shuai Chen +3 more
semanticscholar +1 more source
A Method to Enhance Homogeneous Distribution of Matched Features for Image Matching
International Journal of Environment and Geoinformatics, 2020 Features are distinctive landmarks of an image. There are various feature detection and description algorithms. Many computer vision algorithms require matching of features from two images.
Baran Gulmez +5 more
doaj +1 more source
DeDoDe: Detect, Don’t Describe — Describe, Don’t Detect for Local Feature Matching [PDF]
International Conference on 3D Vision, 2023 Keypoint detection is a pivotal step in 3D reconstruction, whereby sets of (up to) K points are detected in each view of a scene. Crucially, the detected points need to be consistent between views, i.e., correspond to the same 3D point in the scene.
Johan Edstedt +3 more
semanticscholar +1 more source
ClusterGNN: Cluster-based Coarse-to-Fine Graph Neural Network for Efficient Feature Matching [PDF]
Computer Vision and Pattern Recognition, 2022 Graph Neural Networks (GNNs) with attention have been successfully applied for learning visual feature matching. However, current methods learn with complete graphs, resulting in a quadratic complexity in the number of features.
Yanxing Shi +5 more
semanticscholar +1 more source