[1] R. Arandjelovic, P. Gronat, A. Torii, T. Pajdla, and J. Sivic, "NetVLAD: CNN architecture for weakly supervised place recognition," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 5297–5307.
[2] H. Noh, A. Araujo, J. Sim, T. Weyand, and B. Han, "Large-scale image retrieval with attentive deep local features," in Proceedings of the IEEE international conference on computer vision, 2017, pp. 3456–3465.
[3] S. Agarwal et al., "Building rome in a day," Communications of the ACM, vol. 54, no. 10, pp. 105–112, 2011.
[4] S. Zhu et al., "Very large-scale global sfm by distributed motion averaging," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 4568–4577.
[5] L. Cui and C. Ma, "SDF-SLAM: Semantic depth filter SLAM for dynamic environments," IEEE Access, vol. 8, pp. 95301–95311, 2020.
[6] Y. Wang, Q. Huang, C. Jiang, J. Liu, M. Shang, and Z. Miao, "Video stabilization: A comprehensive survey," Neurocomputing, vol. 516, pp. 205–230, 2023.
[7] Y. Wang, Q. Huang, J. Liu, C. Jiang, and M. Shang, "Adaptive video stabilization based on feature point detection and full-reference stability assessment," Multimedia Tools and Applications, vol. 83, no. 11, pp. 32497–32524, 2024.
[8] Z. Zhang, Y. Zhang, Z. Shi, and S. Huang, "Image matching based on region template correlation," Infrared Laser Eng, vol. 33, no. 6, pp. 615–618, 2004.
9] Z. Diao, G. Peng, and L. Fu, "Fast high-precision reliable image matching algorithm based on shape," J. Comput. Appl, vol. 30, no. 2, p. 441, 2010.
[10] Q. Huang, X. Guo, Y. Wang, H. Sun, and L. Yang, "A survey of feature matching methods," IET Image Processing, vol. 18, no. 6, pp. 1385–1410, 2024.
[11] H. P. Moravec, "Rover Visual Obstacle Avoidance," in IJCAI, 1981, vol. 81, pp. 785–790.
[12] C. Schimd and R. Mohr, "Local gray value invariants for image retrieval," IEEE Trans. on Pattern Analysis and Machine Intelligence, pp. 530–534, 1997.
[13] D. G. Lowe, "Object recognition from local scale-invariant features," in Proceedings of the seventh IEEE international conference on computer vision, 1999, vol. 2: Ieee, pp. 1150–1157.
[14] H. Bay, T. Tuytelaars, and L. Van Gool, "Surf: Speeded up robust features," in European conference on computer vision, 2006: Springer, pp. 404–417.
[15] J.-M. Morel and G. Yu, "ASIFT: A new framework for fully affine invariant image comparison," SIAM journal on imaging sciences, vol. 2, no. 2, pp. 438–469, 2009.
[16] C. Lin, J. Liu, and L. Cao, "Image matching by affine speed-up robust features," in MIPPR 2011: Pattern Recognition and Computer Vision, 2011, vol. 8004: SPIE, pp. 120–124.
[17] E. Rublee, V. Rabaud, K. Konolige, and G. Bradski, "ORB: An efficient alternative to SIFT or SURF," in 2011 International conference on computer vision, 2011: Ieee, pp. 2564–2571.
[18] S. Leutenegger, M. Chli, and R. Y. Siegwart, "BRISK: Binary robust invariant scalable keypoints," in 2011 International conference on computer vision, 2011: Ieee, pp. 2548–2555.
[19] P. F. Alcantarilla, A. Bartoli, and A. J. Davison, "KAZE features," in European conference on computer vision, 2012: Springer, pp. 214–227.
[20] D. G. Lowe, "Distinctive image features from scale-invariant keypoints," International journal of computer vision, vol. 60, no. 2, pp. 91–110, 2004.
[21] D. DeTone, T. Malisiewicz, and A. Rabinovich, "Superpoint: Self-supervised interest point detection and description," in Proceedings of the IEEE conference on computer vision and pattern recognition workshops, 2018, pp. 224–236.
[22] P. F. Alcantarilla and T. Solutions, "Fast explicit diffusion for accelerated features in nonlinear scale spaces," IEEE Trans. Patt. Anal. Mach. Intell, vol. 34, no. 7, pp. 1281–1298, 2011.
[23] M. Pultar, "Improving the hardnet descriptor," arXiv preprint arXiv:2007.09699, 2020.
[24] M. A. Fischler and R. C. Bolles, "Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography," Communications of the ACM, vol. 24, no. 6, pp. 381–395, 1981.
[25] D. Barath, J. Noskova, M. Ivashechkin, and J. Matas, "MAGSAC++, a fast, reliable and accurate robust estimator," in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2020, pp. 1304–1312.
[26] R. Hartley and A. Zisserman, Multiple view geometry in computer vision. Cambridge university press, 2003.
[27] P. Lindenberger, P.-E. Sarlin, and M. Pollefeys, "Lightglue: Local feature matching at light speed," in Proceedings of the IEEE/CVF international conference on computer vision, 2023, pp. 17627–17638.
[28] U. o. O. Visual Geometry Group. "Affine Image Transformations." http://www.robots.ox.ac.uk/vgg/affine.html (accessed October 10, 2025).
