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Feature matching is an important computer vision task that involves estimating correspondences between two images of a 3D scene, and dense methods estimate all such correspondences.
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Scale-space theory: A basic tool for analyzing structures at different scales
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Rule-based machine learning methods for functional prediction
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Luís Torgo and João Gama · 1996
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David G Lowe · 2004
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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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Surf: Speeded up robust features
Herbert Bay, Tinne Tuytelaars, and Luc Van Gool · 2006
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Channel smoothing: Efficient robust smoothing of low-level signal features
Michael Felsberg, P-E Forssen, and H Scharr · 2006
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WxBS: Wide Baseline Stereo Generalizations
Dmytro Mishkin, Jiri Matas, Michal Perdoch, and Karel Lenc · 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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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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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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Inloc: Indoor visual localization with dense matching and view synthesis
Hajime Taira, Masatoshi Okutomi, Torsten Sattler, Mircea Cimpoi, Marc Pollefeys, Josef Sivic, Tomas Pajdla, and Akihiko Torii · 2018
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A general and adaptive robust loss function
Jonathan T Barron · 2019
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Large scale joint semantic re-localisation and scene understanding via globally unique instance coordinate regression
Ignas Budvytis, Marvin Teichmann, Tomas Vojir, and Roberto Cipolla · 2019
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D2-Net: A Trainable CNN for Joint Detection and Description of Local Features
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Dgc-net: Dense geometric correspondence network
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R2d2: Reliable and repeatable detector and descriptor
Jerome Revaud, Cesar De Souza, Martin Humenberger, and Philippe Weinzaepfel · 2019
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ASpanFormer: Detector-free image matching with adaptive span transformer
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Masked autoencoders are scalable vision learners
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Image matching challenge 2022, 2022
Addison Howard, Eduard Trulls, Kwang Moo Yi, Dmitry Mishkin, Sohier Dane, and Yuhe Jin · 2022
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Could giant pre-trained image models extract universal representations?
Yutong Lin, Ze Liu, Zheng Zhang, Han Hu, Nanning Zheng, Stephen Lin, and Yue Cao · 2022
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Camliflow: bidirectional camera-lidar fusion for joint optical flow and scene flow estimation
Haisong Liu, Tao Lu, Yihui Xu, Jia Liu, Wenjie Li, and Lijun Chen · 2022
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Quadtree attention for vision transformers
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From coarse to fine: Robust hierarchical localization at large scale
Paul-Edouard Sarlin, Cesar Cadena, Roland Siegwart, and Marcin Dymczyk · 2019
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MAGSAC++, a fast, reliable and accurate robust estimator
Daniel Barath, Jana Noskova, Maksym Ivashechkin, and Jiri Matas · 2020
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Wasserstein distances for stereo disparity estimation
Divyansh Garg, Yan Wang, Bharath Hariharan, Mark Campbell, Kilian Q Weinberger, and Wei-Lun Chao · 2020
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Hierarchical scene coordinate classification and regression for visual localization
Xiaotian Li, Shuzhe Wang, Yi Zhao, Jakob Verbeek, and Juho Kannala · 2020
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Superglue: Learning feature matching with graph neural networks
Paul-Edouard Sarlin, Daniel DeTone, Tomasz Malisiewicz, and Andrew Rabinovich · 2020
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Prior guided feature enrichment network for few-shot segmentation
Zhuotao Tian, Hengshuang Zhao, Michelle Shu, Zhicheng Yang, Ruiyu Li, and Jiaya Jia · 2020
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DISK: learning local features with policy gradient
Michal J. Tyszkiewicz, Pascal Fua, and Eduard Trulls · 2020
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Shitao Tang, Jiahui Zhang, Siyu Zhu, and Ping Tan · 2022
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Proper reuse of image classification features improves object detection
Cristina Vasconcelos, Vighnesh Birodkar, and Vincent Dumoulin · 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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Masked feature prediction for self-supervised visual pre-training
Chen Wei, Haoqi Fan, Saining Xie, Chao-Yuan Wu, Alan Yuille, and Christoph Feichtenhofer · 2022
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ibot: Image bert pre-training with online tokenizer
Jinghao Zhou, Chen Wei, Huiyu Wang, Wei Shen, Cihang Xie, Alan Yuille, and Tao Kong · 2022
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Improving transformer-based image matching by cascaded capturing spatially informative keypoints
Chenjie Cao and Yanwei Fu · 2023
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DKM: Dense kernelized feature matching for geometry estimation
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SiLK: Simple Learned Keypoints
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LightGlue: Local Feature Matching at Light Speed
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Pats: Patch area transportation with subdivision for local feature matching
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DINOv2: Learning robust visual features without supervision
Maxime Oquab, Timothée Darcet, Theo Moutakanni, Huy V. Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, Russell Howes, Po-Yao Huang, Hu Xu, Vasu Sharma, Shang-Wen Li, Wojciech Galuba, Mike Rabbat, Mido Assran, Nicolas Ballas, Gabriel Synnaeve, Ishan Misra, Herve Jegou, Julien Mairal, Patrick Labatut, Armand Joulin, and Piotr Bojanowski · 2023
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ASTR: Adaptive spot-guided transformer for consistent local feature matching
Jiahuan Yu, Jiahao Chang, Jianfeng He, Tianzhu Zhang, Jiyang Yu, and Wu Feng · 2023
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PMatch: Paired masked image modeling for dense geometric matching
Shengjie Zhu and Xiaoming Liu · 2023
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