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Stereo matching is a fundamental building block for many vision and robotics applications.
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S. Duggal, S. Wang, W.-C. Ma, R. Hu, and R. Urtasun, “Deeppruner: Learning efficient stereo matching via differentiable patchmatch,” in
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Z. Liang, Y. Guo, Y. Feng, W. Chen, L. Qiao, L. Zhou, J. Zhang, and H. Liu, “Stereo matching using multi-level cost volume and multi-scale feature constancy,”
2019
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X. Cheng, P. Wang, and R. Yang, “Learning depth with convolutional spatial propagation network,”
2019
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Z. Wu, X. Wu, X. Zhang, S. Wang, and L. Ju, “Semantic stereo matching with pyramid cost volumes,” in
2019
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Y. Yao, Z. Luo, S. Li, T. Shen, T. Fang, and L. Quan, “Recurrent mvsnet for high-resolution multi-view stereo depth inference,” in
2019
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F. Zhang, V. Prisacariu, R. Yang, and P. H. Torr, “Ga-net: Guided aggregation net for end-to-end stereo matching,” in
2019
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X. Gu, Z. Fan, S. Zhu, Z. Dai, F. Tan, and P. Tan, “Cascade cost volume for high-resolution multi-view stereo and stereo matching,” in
2020
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S. Cheng, Z. Xu, S. Zhu, Z. Li, L. E. Li, R. Ramamoorthi, and H. Su, “Deep stereo using adaptive thin volume representation with uncertainty awareness,” in
2020
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C. Yao, Y. Jia, H. Di, P. Li, and Y. Wu, “A decomposition model for stereo matching,” in
2021
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V. Tankovich, C. Hane, Y. Zhang, A. Kowdle, S. Fanello, and S. Bouaziz, “Hitnet: Hierarchical iterative tile refinement network for real-time stereo matching,” in
2021
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2021
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L. Lipson, Z. Teed, and J. Deng, “Raft-stereo: Multilevel recurrent field transforms for stereo matching,” in
2021
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G. Xu, J. Cheng, P. Guo, and X. Yang, “Attention concatenation volume for accurate and efficient stereo matching,” in
2022
Closest in time.
G. Xu, X. Wang, X. Ding, and X. Yang, “Iterative geometry encoding volume for stereo matching,” in
2023
Closest in time.
J. Cheng, G. Xu, P. Guo, and X. Yang, “Coatrsnet: Fully exploiting convolution and attention for stereo matching by region separation,”
2023
Closest in time.