The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.
Learning to Find Good Correspondences
Open accessAbstract
We develop a deep architecture to learn to find good correspondences for wide-baseline stereo. Given a set of putative sparse matches and the camera intrinsics, we train our network in an end-to-end fashion to label the correspondences as inliers or outliers, while simultaneously using them to recover the relative pose, as encoded by the essential matrix. Our architecture is based on a multi-layer perceptron operating on pixel coordinates rather than directly on the image, and is thus simple and small. We introduce a novel normalization technique, called Context Normalization, which allows us to process each data point separately while embedding global information in it, and also makes the network invariant to the order of the correspondences. Our experiments on multiple challenging datasets demonstrate that our method is able to drastically improve the state of the art with little training data.
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Distinctive Image Features from Scale-Invariant Keypoints
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Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818
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Multiple View Geometry in Computer Vision
Hartley, R., & Zisserman, A. (2004) · doi:10.1017/cbo9780511811685 · Cited by 21,070
SURF: Speeded Up Robust Features
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Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S. (2024) · arXiv (Cornell University) · doi:10.57702/o9raffed · Cited by 14,214
ORB: An efficient alternative to SIFT or SURF
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Robust Regression and Outlier Detection
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Robust Regression and Outlier Detection
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Cited by
LoFTR: Detector-Free Local Feature Matching with Transformers
Sun, J., Shen, Z., Wang, Y., Bao, H., & Zhou, X. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00881 · Cited by 1,719
Image Matching from Handcrafted to Deep Features: A Survey
Ma, J., Jiang, X., Fan, A., Jiang, J., & Yan, J. (2021) · International Journal of Computer Vision · doi:10.1007/s11263-020-01359-2 · Cited by 990
Choy, C., Dong, W., & Koltun, V. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00259 · Cited by 548
Normalization Techniques in Training DNNs: Methodology, Analysis and Application
Huang, L., Qin, J., Zhou, Y., Zhu, F., Liu, L., & Shao, L. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3250241 · Cited by 537
A review of multimodal image matching: Methods and applications
Jiang, X., Ma, J., Xiao, G., Shao, Z., & Guo, X. (2021) · Information Fusion · doi:10.1016/j.inffus.2021.02.012 · Cited by 532
Metadata
- Authors
- Yi, K. M., Trulls, E., Ono, Y., Lepetit, V., Salzmann, M., & Fua, P.
- Venue
- 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2018-06
- Pages
- 2666-2674
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2768360176
- Cited by
- 552
- References
- 36
- FWCI
- 16.23
- Type
- conference paper
- Language
- en
- Open access
- green
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- Yi, K. M., Trulls, E., Ono, Y., Lepetit, V., Salzmann, M., & Fua, P. (2018). Learning to Find Good Correspondences. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/cvpr.2018.00282
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