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Image matching is a classic and fundamental task in computer vision.
“Yfcc100m: The new data in multimedia research,”
Bart Thomee, David A Shamma, Gerald Friedland, Benjamin Elizalde, Karl Ni, Douglas Poland, Damian Borth, and Li-Jia Li, · 2016
Earlier work this paper cites.
“Superpoint: Self-supervised interest point detection and description,”
Daniel DeTone, Tomasz Malisiewicz, and Andrew Rabinovich, · 2018
Earlier work this paper cites.
“Learning to find good correspondences,”
Kwang Moo Yi, Eduard Trulls, Yuki Ono, Vincent Lepetit, Mathieu Salzmann, and Pascal Fua, · 2018
Earlier work this paper cites.
“Neural-guided ransac: Learning where to sample model hypotheses,”
Eric Brachmann and Carsten Rother, · 2019
Earlier work this paper cites.
“Learning two-view correspondences and geometry using order-aware network,”
Jiahui Zhang, Dawei Sun, Zixin Luo, Anbang Yao, Lei Zhou, Tianwei Shen, Yurong Chen, Long Quan, and Hongen Liao, · 2019
Cited alongside, same era.
“Disk: Learning local features with policy gradient,”
Michał Tyszkiewicz, Pascal Fua, and Eduard Trulls, · 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.
“Loftr: Detector-free local feature matching with transformers,”
Jiaming Sun, Zehong Shen, Yuang Wang, Hujun Bao, and Xiaowei Zhou, · 2021
Cited alongside, same era.
“Deep kernelized dense geometric matching,”
Johan Edstedt, Mårten Wadenbäck, and Michael Felsberg, · 2022
Later among the works it cites.
“Quadtree attention for vision transformers,”
Shitao Tang, Jiahui Zhang, Siyu Zhu, and Ping Tan, · 2022
Later among the works it cites.
“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
Later among the works it cites.
“Megadepth: Learning single-view depth prediction from internet photos,”
Zhengqi Li and Noah Snavely, · 2050
Closest in time.
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