IEEE/CAA Journal of Automatica Sinica
Citation: | G. Wang and Y. F. Chen, “MCNet: Multiscale clustering network for two-view geometry learning and feature matching,” IEEE/CAA J. Autom. Sinica, vol. 10, no. 6, pp. 1507–1509, Jun. 2023. doi: 10.1109/JAS.2023.123144 |
[1] |
D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” Int. J. Computer Vision, vol. 60, no. 2, pp. 91–110, 2004. doi: 10.1023/B:VISI.0000029664.99615.94
|
[2] |
D. DeTone, T. Malisiewicz, and A. Rabinovich, “Superpoint: Self-supervised interest point detection and description,” in Proc. IEEE Conf. Computer Vision Pattern Recognition Workshops, 2018, pp. 224–236.
|
[3] |
M. A. Fischler and R. C. Bolles, “Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography,” Communications ACM, vol. 24, no. 6, pp. 381–395, 1981. doi: 10.1145/358669.358692
|
[4] |
K. M. Yi, E. Trulls, Y. Ono, V. Lepetit, M. Salzmann, and P. Fua, “Learning to find good correspondences,” in Proc. IEEE Conf. Computer Vision Pattern Recognition, 2018, pp. 2666–2674.
|
[5] |
C. R. Qi, H. Su, K. Mo, and L. J. Guibas, “PointNet: Deep learning on point sets for 3D classification and segmentation,” in Proc. IEEE Conf. Computer Vision Pattern Recognition, 2017, pp. 652–660.
|
[6] |
R. Ranftl and V. Koltun, “Deep fundamental matrix estimation,” in Proc. European Conf. Computer Vision, 2018, pp. 284–299.
|
[7] |
T. Plötz and S. Roth, “Neural nearest neighbors networks,” Advances Neural Information Processing Syst, vol. 31, pp. 1–12, 2018.
|
[8] |
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 Proc. IEEE Int. Conf. Computer Vision, 2019, pp. 5845–5854.
|
[9] |
J. Ma, Y. Wang, A. Fan, G. Xiao, and R. Chen, “Correspondence attention transformer: A context-sensitive network for two-view correspondence learning,” IEEE Trans. Multimedia, pp. 1–16, 2022. DOI: 10.1109/TMM.2022.3162115
|
[10] |
W. Sun, W. Jiang, E. Trulls, A. Tagliasacchi, and K. M. Yi, “ACNe: Attentive context normalization for robust permutation-equivariant learning,” in Proc. IEEE/CVF Conf. Computer Vision Pattern Recognition, 2020, pp. 11286–11295.
|
[11] |
Z. Ying, J. You, C. Morris, X. Ren, W. Hamilton, and J. Leskovec, “Hierarchical graph representation learning with differentiable pooling,” Advances Neural Information Processing Systems, vol. 31, pp. 1–11, 2018.
|
[12] |
O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional networks for biomedical image segmentation,” in Proc. Int. Conf. Medical Image Computing Computer-Assisted Intervention, Springer, 2015, pp. 234–241.
|
[13] |
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 ACM, vol. 59, no. 2, pp. 64–73, 2016. doi: 10.1145/2812802
|
[14] |
J. Heinly, J. L. Schonberger, E. Dunn, and J.-M. Frahm, “Reconstructing the world in six days,” in Proc. IEEE Conf. Computer Vision Pattern Recognition, 2015, pp. 3287–3295.
|
[15] |
J. Xiao, A. Owens, and A. Torralba, “Sun3D: A database of big spaces reconstructed using SFM and object labels,” in Proc. IEEE Int. Conf. Computer Vision, 2013, pp. 1625–1632.
|
[16] |
C. Wu, “Towards linear-time incremental structure from motion,” in Proc. IEEE Int. Conf. 3D Vision, 2013, pp. 127–134.
|
[17] |
D. Barath, J. Matas, and J. Noskova, “Magsac: Marginalizing sample consensus,” in Proc. IEEE Conf. Computer Vision Pattern Recognition, 2019, pp. 10197–10205.
|
[18] |
J. Ma, J. Zhao, J. Jiang, H. Zhou, and X. Guo, “Locality preserving matching,” Int. J. Computer Vision, vol. 127, no. 5, pp. 512–531, 2019. doi: 10.1007/s11263-018-1117-z
|
[19] |
G. Wang and Y. Chen, “Robust feature matching using guided local outlier factor,” Pattern Recognition, vol. 117, p. 107986, 2021. doi: 10.1016/j.patcog.2021.107986
|