[35] Yurii Evgen'evich Nesterov. A method of solving a convex programming problem with convergence rate  $\mathrm{o}(\mathrm{k}^2)$ . In Doklady Akademii Nauk, volume 269, pages 543-547, 1983.
[36] Trung T Pham, Tat-Jun Chin, Jin Yu, and David Suter. The random cluster model for robust geometric fitting. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(8):1658-1671, 2014.
[37] Thomas Probst, Ajad Chhatkuli, Danda Pani Paudel, and Luc Van Gool. What correspondences reveal about unknown camera and motion models? In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 8730-8738, 2019.
[38] Rahul Raguram, Ondrej Chum, Marc Pollefeys, Jiri Matas, and Jan-Michael Frahm. Usac: a universal framework for random sample consensus. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8):2022-2038, 2012.
[39] Muhamad Risqi U Saputra, Andrew Markham, and Niki Trigoni. Visual slam and structure from motion in dynamic environments: A survey. ACM Computing Surveys, 51(2):1-36, 2018.
[40] Paul-Edouard Sarlin, Daniel DeTone, Tomasz Malisiewicz, and Andrew Rabinovich. Superglue: Learning feature matching with graph neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 4938–4947, 2020.
[41] Konrad Schindler and David Suter. Two-view multibody structure-and-motion with outliers through model selection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(6):983-995, 2006.
[42] Gideon Schwarz. Estimating the dimension of a model. The Annals of Statistics, 6(2):461-464, 1978.
[43] Weiwei Sun, Wei Jiang, Eduard Trulls, Andrea Tagliasacchi, and Kwang Moo Yi. Acne: Attentive context normalization for robust permutation-equivariant learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 11286-11295, 2020.
[44] Philip HS Torr. Geometric motion segmentation and model selection. Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences, 356(1740): 1321-1340, 1998.
[45] Manolis C Tsakiris and René Vidal. Dual principal component pursuit. In Proceedings of the IEEE International Conference on Computer Vision Workshops, pages 10-18, 2015.
[46] Manolis C Tsakiris and René Vidal. Dual principal component pursuit. Journal of Machine Learning Research, 19:1-49, 2018.
[47] René Vidal and Paolo Favaro. Low rank subspace clustering (lrsc). Pattern Recognition Letters, 43:47-61, 2014.
[48] Yong Wang, Yuan Jiang, Yi Wu, and Zhi-Hua Zhou. Spectral clustering on multiple manifolds. IEEE Transactions on Neural Networks, 22(7):1149-1161, 2011.
[49] Kwang Moo Yi, Eduard Trulls, Yuki Ono, Vincent Lepetit, Mathieu Salzmann, and Pascal Fua. Learning to find good correspondences. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2666-2674, 2018.
[50] Jiahui Zhang, Dawei Sun, Zixin Luo, Anbang Yao, Lei Zhou, Tianwei Shen, Yurong Chen, Long Quan, and Hongen Liao. Learning two-view correspondences and geometry using order-aware network. In Proceedings of the IEEE International Conference on Computer Vision, pages 5845-5854, 2019.
[51] Shihua Zhang and Jiayi Ma. Convmatch: Rethinking network design for two-view correspondence learning. In Proc. AAAI Conf. Artif. Intell, pages 1-12, 2023.
[52] Zhihui Zhu, Yifan Wang, Daniel Robinson, Daniel Naiman, Rene Vidal, Manolis C Tsakiris, et al. Dual principal component pursuit: Improved analysis and efficient algorithms. In Advances in Neural Information Processing Systems, pages 2171-2181, 2018.