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Feature matching is a crucial technique in computer vision.
Z. Zhong, G. Xiao, L. Zheng, Y. Lu, and J. Ma, “T-net: Effective permutation-equivariant network for two-view correspondence learning,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , October 2021, pp. 1950–1959
1959
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R. Hartley and A. Zisserman, “Multiple view geometry in computer vision,” Cambridge university press , 2003
2003
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D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” International journal of computer vision , vol. 60, no. 2, pp. 91–110, 2004
2004
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E. Rublee, V. Rabaud, K. Konolige, and G. Bradski, “Orb: An efficient alternative to sift or surf,” 2011 International conference on computer vision , pp. 2564–2571, 2011
2011
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M. Calonder, V. Lepetit, M. Ozuysal, and P. Fua, “Brief: Computing a local binary descriptor very fast,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 34, no. 7, pp. 1281–1298, 2011
2011
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T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick, “Microsoft coco: Common objects in context,” in Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13 . Springer, 2014, pp. 740–755
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C. Cadena, L. Carlone, H. Carrillo, Y. Latif, D. Scaramuzza, J. Neira, I. Reid, and J. J. Leonard, “Past, present, and future of simultaneous localization and mapping: Toward the robust-perception age,” IEEE Transactions on Robotics , vol. 32, no. 6, pp. 1309–1332, 2016
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J. L. Schonberger and J.-M. Frahm, “Structure-from-motion revisited,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 4104–4113
2016
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B. Thomee, D. A. Shamma, G. Friedland, B. Elizalde, K. Ni, D. Poland, D. Borth, and L.-J. Li, “YFCC100M: The new data in multimedia research,” Communications of the ACM , vol. 59, no. 2, pp. 64–73, 2016
2016
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A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems , vol. 30, 2017
2017
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A. Dai, A. X. Chang, M. Savva, M. Halber, T. Funkhouser, and M. Nießner, “Scannet: Richly-annotated 3d reconstructions of indoor scenes,” Proc. Computer Vision and Pattern Recognition (CVPR), IEEE , 2017
2017
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A. Chang, A. Dai, T. Funkhouser, M. Halber, M. Niessner, M. Savva, S. Song, A. Zeng, and Y. Zhang, “Matterport3d: Learning from rgb-d data in indoor environments,” International Conference on 3D Vision (3DV) , 2017
2017
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D. DeTone, T. Malisiewicz, and A. Rabinovich, “Superpoint: Self-supervised interest point detection and description,” Proceedings of the IEEE conference on computer vision and pattern recognition workshops , pp. 224–236, 2018
2018
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I. Rocco, M. Cimpoi, R. Arandjelović, A. Torii, T. Pajdla, and J. Sivic, “Neighbourhood consensus networks,” Advances in neural information processing systems , vol. 31, 2018
2018
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P. Truong, S. Apostolopoulos, A. Mosinska, S. Stucky, C. Ciller, and S. D. Zanet, “Glampoints: Greedily learned accurate match points,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 10 732–10 741
2019
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J. Revaud, C. De Souza, M. Humenberger, and P. Weinzaepfel, “R2d2: Reliable and repeatable detector and descriptor,” Advances in neural information processing systems , vol. 32, 2019
2019
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M. Dusmanu, I. Rocco, T. Pajdla, M. Pollefeys, J. Sivic, A. Torii, and T. Sattler, “D2-net: A trainable cnn for joint description and detection of local features,” Proceedings of the ieee/cvf conference on computer vision and pattern recognition , pp. 8092–8101, 2019
2019
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A. Barroso-Laguna, E. Riba, D. Ponsa, and K. Mikolajczyk, “Key. net: Keypoint detection by handcrafted and learned cnn filters,” Proceedings of the IEEE/CVF International Conference on Computer Vision , pp. 5836–5844, 2019
2019
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J. Zhang, D. Sun, Z. Luo, A. Yao, L. Zhou, T. Shen, Y. Chen, L. Quan, and H. Liao, “Learning two-view correspondences and geometry using order-aware network,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , October 2019
2019
Cited alongside, same era.
Z. Luo, L. Zhou, X. Bai, H. Chen, J. Zhang, Y. Yao, S. Li, T. Fang, and L. Quan, “Aslfeat: Learning local features of accurate shape and localization,” Computer Vision and Pattern Recognition (CVPR) , 2020
T. Ng, H. J. Kim, V. T. Lee, D. DeTone, T.-Y. Yang, T. Shen, E. Ilg, V. Balntas, K. Mikolajczyk, and C. Sweeney, “Ninjadesc: Content-concealing visual descriptors via adversarial learning,” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pp. 12 797–12 807, 2022
2022
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J. Revaud, V. Leroy, P. Weinzaepfel, and B. Chidlovskii, “Pump: Pyramidal and uniqueness matching priors for unsupervised learning of local descriptors,” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pp. 3926–3936, 2022
2022
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K. Li, L. Wang, L. Liu, Q. Ran, K. Xu, and Y. Guo, “Decoupling makes weakly supervised local feature better,” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pp. 15 838–15 848, 2022
2022
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Y. Xia and J. Ma, “Locality-guided global-preserving optimization for robust feature matching,” IEEE Transactions on Image Processing , vol. 31, pp. 5093–5108, 2022
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2020
Cited alongside, same era.
M. Tyszkiewicz, P. Fua, and E. Trulls, “Disk: Learning local features with policy gradient,” Advances in Neural Information Processing Systems , vol. 33, pp. 14 254–14 265, 2020
2020
Cited alongside, same era.
P.-E. Sarlin, D. DeTone, T. Malisiewicz, and A. Rabinovich, “Superglue: Learning feature matching with graph neural networks,” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2020
2020
Cited alongside, same era.
I. Rocco, R. Arandjelović, and J. Sivic, “Efficient neighbourhood consensus networks via submanifold sparse convolutions,” European conference on computer vision , pp. 605–621, 2020
2020
Cited alongside, same era.
L. Xinghui, H. Kai, L. Shuda, and V. P. Adrian, “Dual-resolution correspondence networks,” NIPS , pp. 17 346–17 357, 2020
2020
Cited alongside, same era.
J. Ma, X. Jiang, A. Fan, J. Jiang, and J. Yan, “Image matching from handcrafted to deep features: A survey,” International Journal of Computer Vision , vol. 129, no. 1, pp. 23–79, 2021
2021
Cited alongside, same era.
J. Sun, Z. Shen, Y. Wang, H. Bao, and X. Zhou, “LoFTR: Detector-free local feature matching with transformers,” CVPR , 2021
2021
Cited alongside, same era.
W. Jiang, E. Trulls, J. Hosang, A. Tagliasacchi, and K. M. Yi, “Cotr: Correspondence transformer for matching across images,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 6207–6217
2021
Cited alongside, same era.
X. Zhao, X. Wu, J. Miao, W. Chen, P. C. Chen, and Z. Li, “Alike: Accurate and lightweight keypoint detection and descriptor extraction,” IEEE Transactions on Multimedia , 2022
2022
Cited alongside, same era.
2022
Later among the works it cites.
L. Zheng, G. Xiao, Z. Shi, S. Wang, and J. Ma, “Msa-net: Establishing reliable correspondences by multiscale attention network,” IEEE Transactions on Image Processing , vol. 31, pp. 4598–4608, 2022
2022
Later among the works it cites.
S. Tang, J. Zhang, S. Zhu, and P. Tan, “Quadtree attention for vision transformers,” ICLR , 2022
2022
Later among the works it cites.
Y. Liao, J. Xie, and A. Geiger, “Kitti-360: A novel dataset and benchmarks for urban scene understanding in 2d and 3d,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2022
2022
Later among the works it cites.
J. Yang, C. Li, X. Dai, and J. Gao, “Focal modulation networks,” Advances in Neural Information Processing Systems (NeurIPS) , 2022
2022
Later among the works it cites.
J. Edstedt, I. Athanasiadis, M. Wadenbäck, and M. Felsberg, “DKM: Dense kernelized feature matching for geometry estimation,” CVPR , 2023
2023
Closest in time.
2023
Closest in time.
Y. L. Junjie Ni, H. L. Zhaoyang Huang, Z. C. Hujun Bao, and G. Zhang, “Pats: Patch area transportation with subdivision for local feature matching,” in The IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR) , 2023
2023
Closest in time.
F. Xue, I. Budvytis, and R. Cipolla, “Sfd2: Semantic-guided feature detection and description,” in CVPR , 2023
2023
Closest in time.
K. T. Giang, S. Song, and S. Jo, “Topicfm: Robust and interpretable topic-assisted feature matching,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 37, no. 2, 2023, pp. 2447–2455
2023
Closest in time.
2023
Closest in time.
X. Liu, G. Xiao, R. Chen, and J. Ma, “Pgfnet: Preference-guided filtering network for two-view correspondence learning,” IEEE Transactions on Image Processing , vol. 32, pp. 1367–1378, 2023
2023
Closest in time.