NeFSAC: Neurally Filtered Minimal Samples-Reference-Cited by-同舟云学术

NeFSAC: Neurally Filtered Minimal Samples

Published:2022 Issue: Volume: Page:351-366
ISSN:0302-9743
Container-title:Lecture Notes in Computer Science
language:
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Author:

Cavalli Luca^ORCID,Pollefeys Marc^ORCID,Barath Daniel^ORCID

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-19824-3_21

Reference38 articles.

1. Barath, D., Chin, T.J., Chum, O., Mishkin, D., Ranftl, R., Matas, J.: RANSAC in 2020 tutorial. In: CVPR (2020). http://cmp.felk.cvut.cz/cvpr2020-ransac-tutorial/

2. Barath, D., Matas, J.: Graph-cut RANSAC. In: CVPR, pp. 6733–6741 (2018)

3. Barath, D., Noskova, J., Ivashechkin, M., Matas, J.: MAGSAC++, a fast, reliable and accurate robust estimator. In: CVPR, pp. 1304–1312 (2020)

4. Barath, D., Noskova, J., Matas, J.: MAGSAC: marginalizing sample consensus. In: CVPR, pp. 10197–10205 (2019). https://github.com/danini/magsac

5. Barath, D., Noskova, J., Matas, J.: Marginalizing sample consensus. IEEE Trans. Pattern Anal. Mach. Intell. 44(11), 8420–8432 (2021)

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