Two-view correspondence learning using graph neural network with reciprocal neighbor attention-Reference-Cited by-同舟云学术

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Two-view correspondence learning using graph neural network with reciprocal neighbor attention

Published:2023-08 Issue: Volume:202 Page:114-124
ISSN:0924-2716
Container-title:ISPRS Journal of Photogrammetry and Remote Sensing
language:en
Short-container-title:ISPRS Journal of Photogrammetry and Remote Sensing

Author:

Li Zizhuo,Ma Yong,Mei Xiaoguang^ORCID,Ma Jiayi^ORCID

Funder

Guangxi Key Research and Development Program

Major Science and Technology Projects in Anhui Province

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Computers in Earth Sciences,Computer Science Applications,Engineering (miscellaneous),Atomic and Molecular Physics, and Optics

Reference54 articles.

1. Balntas, V., Lenc, K., Vedaldi, A., Mikolajczyk, K., 2017. HPatches: A benchmark and evaluation of handcrafted and learned local descriptors. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 5173–5182.

2. Barath, D., Matas, J., 2018. Graph-cut RANSAC. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 6733–6741.

3. Barath, D., Matas, J., Noskova, J., 2019. MAGSAC: Marginalizing sample consensus. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 10197–10205.

4. Barath, D., Noskova, J., Ivashechkin, M., Matas, J., 2020. MAGSAC++, a fast, reliable and accurate robust estimator. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 1304–1312.

5. Bian, J., Lin, W.-Y., Matsushita, Y., Yeung, S.-K., Nguyen, T.-D., Cheng, M.-M., 2017. Gms: Grid-based motion statistics for fast, ultra-robust feature correspondence. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 4181–4190.

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1. MC-Net: Integrating Multi-Level Geometric Context for Two-View Correspondence Learning;IEEE Transactions on Circuits and Systems for Video Technology;2024-08

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