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Feature matching is a challenging computer vision task that involves finding correspondences between two images of a 3D scene.
Discrimination thresholds for channel-coded systems
Herman P Snippe and Jan J Koenderink · 1992
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Distinctive image features from scale-invariant keypoints
David G Lowe · 2004
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Two-view geometry estimation unaffected by a dominant plane
Ondrej Chum, Tomas Werner, and Jiri Matas · 2005
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Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Carl Edward Rasmussen and Christopher K. I. Williams · 2005
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Random features for large-scale kernel machines
Ali Rahimi and Benjamin Recht · 2007
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Fast and accurate structure and motion estimation
Johan Hedborg, Per-Erik Forssén, and Michael Felsberg · 2009
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Kernels for vector-valued functions: A review
Mauricio A. Álvarez, Lorenzo Rosasco, and Neil D. Lawrence · 2012
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ORB-SLAM: a versatile and accurate monocular slam system
Raul Mur-Artal, Jose Maria Martinez Montiel, and Juan D Tardos · 2015
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Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, et al · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Structure-from-motion revisited
Johannes L Schonberger and Jan-Michael Frahm · 2016
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HPatches: A benchmark and evaluation of handcrafted and learned local descriptors
Vassileios Balntas, Karel Lenc, Andrea Vedaldi, and Krystian Mikolajczyk · 2017
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A minimal solution for two-view focal-length estimation using two affine correspondences
Daniel Barath, Tekla Toth, and Levente Hajder · 2017
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Scannet: Richly-annotated 3d reconstructions of indoor scenes
Angela Dai, Angel X Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, and Matthias Nießner · 2017
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
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Superpoint: Self-supervised interest point detection and description
Daniel DeTone, Tomasz Malisiewicz, and Andrew Rabinovich · 2018
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Megadepth: Learning single-view depth prediction from internet photos
Zhengqi Li and Noah Snavely · 2018
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Neighbourhood consensus networks
I. Rocco, M. Cimpoi, R. Arandjelović, A. Torii, T. Pajdla, and J. Sivic · 2018
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Benchmarking 6dof outdoor visual localization in changing conditions
Torsten Sattler, Will Maddern, Carl Toft, Akihiko Torii, Lars Hammarstrand, Erik Stenborg, Daniel Safari, Masatoshi Okutomi, Marc Pollefeys, Josef Sivic, et al · 2018
Cited alongside, same era.
Learning a discriminative feature network for semantic segmentation
Changqian Yu, Jingbo Wang, Chao Peng, Changxin Gao, Gang Yu, and Nong Sang · 2018
Cited alongside, same era.
D2-Net: A Trainable CNN for Joint Detection and Description of Local Features
Mihai Dusmanu, Ignacio Rocco, Tomas Pajdla, Marc Pollefeys, Josef Sivic, Akihiko Torii, and Torsten Sattler · 2019
Cited alongside, same era.
Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2019
Cited alongside, same era.
Dgc-net: Dense geometric correspondence network
Iaroslav Melekhov, Aleksei Tiulpin, Torsten Sattler, Marc Pollefeys, Esa Rahtu, and Juho Kannala · 2019
Cited alongside, same era.
Making affine correspondences work in camera geometry computation
LoFTR: Detector-free local feature matching with transformers
Jiaming Sun, Zehong Shen, Yuang Wang, Hujun Bao, and Xiaowei Zhou · 2021
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𝕏 \mathbb{X} resolution correspondence networks
Georgi Tinchev, Shuda Li, Kai Han, David Mitchell, and Rigas Kouskouridas · 2021
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PDC-Net+: Enhanced Probabilistic Dense Correspondence Network
Prune Truong, Martin Danelljan, Radu Timofte, and Luc Van Gool · 2021
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Learning accurate dense correspondences and when to trust them
Prune Truong, Martin Danelljan, Luc Van Gool, and Radu Timofte · 2021
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Co-attention for conditioned image matching
Olivia Wiles, Sebastien Ehrhardt, and Andrew Zisserman · 2021
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Patch2pix: Epipolar-guided pixel-level correspondences
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Daniel Barath, Michal Polic, Wolfgang Förstner, Torsten Sattler, Tomas Pajdla, and Zuzana Kukelova · 2020
Cited alongside, same era.
S2DNet: learning image features for accurate sparse-to-dense matching
Hugo Germain, Guillaume Bourmaud, and Vincent Lepetit · 2020
Cited alongside, same era.
Minimal solutions for relative pose with a single affine correspondence
Banglei Guan, Ji Zhao, Zhang Li, Fang Sun, and Friedrich Fraundorfer · 2020
Cited alongside, same era.
Large-scale, real-time visual–inertial localization revisited
Simon Lynen, Bernhard Zeisl, Dror Aiger, Michael Bosse, Joel Hesch, Marc Pollefeys, Roland Siegwart, and Torsten Sattler · 2020
Cited alongside, same era.
Efficient neighbourhood consensus networks via submanifold sparse convolutions
Ignacio Rocco, Relja Arandjelović, and Josef Sivic · 2020
Cited alongside, same era.
Superglue: Learning feature matching with graph neural networks
Paul-Edouard Sarlin, Daniel DeTone, Tomasz Malisiewicz, and Andrew Rabinovich · 2020
Cited alongside, same era.
Ransac-flow: generic two-stage image alignment
Xi Shen, François Darmon, Alexei A Efros, and Mathieu Aubry · 2020
Cited alongside, same era.
Qunjie Zhou, Torsten Sattler, and Laura Leal-Taixe · 2021
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Learning to find good models in ransac
Daniel Barath, Luca Cavalli, and Marc Pollefeys · 2022
Closest in time.
Nefsac: Neurally filtered minimal samples
Luca Cavalli, Marc Pollefeys, and Daniel Barath · 2022
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ASpanFormer: Detector-free image matching with adaptive span transformer
Hongkai Chen, Zixin Luo, Lei Zhou, Yurun Tian, Mingmin Zhen, Tian Fang, David Mckinnon, Yanghai Tsin, and Long Quan · 2022
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On the instability of relative pose estimation and ransac’s role
Hongyi Fan, Joe Kileel, and Benjamin Kimia · 2022
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TopicFM: Robust and interpretable topic-assisted feature matching
Khang Truong Giang, Soohwan Song, and Sungho Jo · 2022
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DenseGAP: Graph-Structured Dense Correspondence Learning with Anchor Points
Zhengfei Kuang, Jiaman Li, Mingming He, Tong Wang, and Yajie Zhao · 2022
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Swin transformer v2: Scaling up capacity and resolution
Ze Liu, Han Hu, Yutong Lin, Zhuliang Yao, Zhenda Xie, Yixuan Wei, Jia Ning, Yue Cao, Zheng Zhang, Li Dong, et al · 2022
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3DG-STFM: 3d geometric guided student-teacher feature matching
Runyu Mao, Chen Bai, Yatong An, Fengqing Zhu, and Cheng Lu · 2022
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ECO-TR: Efficient Correspondences Finding Via Coarse-to-Fine Refinement
Dongli Tan, Jiang-Jiang Liu, Xingyu Chen, Chao Chen, Ruixin Zhang, Yunhang Shen, Shouhong Ding, and Rongrong Ji · 2022
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Quadtree attention for vision transformers
Shitao Tang, Jiahui Zhang, Siyu Zhu, and Ping Tan · 2022
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MatchFormer: Interleaving attention in transformers for feature matching
Qing Wang, Jiaming Zhang, Kailun Yang, Kunyu Peng, and Rainer Stiefelhagen · 2022
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