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Stereo matching is close to hitting a half-century of history, yet witnessed a rapid evolution in the last decade thanks to deep learning.
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M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, “Mobilenetv2: Inverted residuals and linear bottlenecks,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 4510–4520
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G. Yang, H. Zhao, J. Shi, Z. Deng, and J. Jia, “Segstereo: Exploiting semantic information for disparity estimation,” in Proceedings of the European conference on computer vision (ECCV) , 2018, pp. 636–651
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J.-R. Chang and Y.-S. Chen, “Pyramid stereo matching network,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 5410–5418
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E. Ilg, T. Saikia, M. Keuper, and T. Brox, “Occlusions, motion and depth boundaries with a generic network for disparity, optical flow or scene flow estimation,” in Proceedings of the European conference on computer vision (ECCV) , 2018, pp. 614–630
2018
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S. Tulyakov, F. Fleuret, M. Kiefel, P. Gehler, and M. Hirsch, “Learning an event sequence embedding for event-based deep stereo,” in Proceedings of the IEEE International Conference on Computer Vision (ICCV) , 2019, Oral, to appear. [Online]. Available: https://fleuret.org/papers/tulyakov-et-al-iccv2019.pdf
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H. Jiang, D. Sun, V. Jampani, Z. Lv, E. Learned-Miller, and J. Kautz, “Sense: A shared encoder network for scene-flow estimation,” in Proceedings of the IEEE/CVF international conference on computer vision , 2019, pp. 3195–3204
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C. Chen, X. Chen, and H. Cheng, “On the over-smoothing problem of cnn based disparity estimation,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 8997–9005
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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 Proceedings of the IEEE/CVF international conference on computer vision , 2019, pp. 4384–4393
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X. Cheng, P. Wang, and R. Yang, “Learning depth with convolutional spatial propagation network,” IEEE transactions on pattern analysis and machine intelligence , vol. 42, no. 10, pp. 2361–2379, 2019
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 Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 185–194
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G. Yang, J. Manela, M. Happold, and D. Ramanan, “Hierarchical deep stereo matching on high-resolution images,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 5515–5524
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M. Mehltretter and C. Heipke, “Cnn-based cost volume analysis as confidence measure for dense matching,” in Proceedings of the IEEE/CVF international conference on computer vision workshops , 2019, pp. 0–0
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H. Laga, L. V. Jospin, F. Boussaid, and M. Bennamoun, “A survey on deep learning techniques for stereo-based depth estimation,” IEEE transactions on pattern analysis and machine intelligence , vol. 44, no. 4, pp. 1738–1764, 2020
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M. Yang, F. Wu, and W. Li, “Waveletstereo: Learning wavelet coefficients of disparity map in stereo matching,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2020
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X. Cheng, Y. Zhong, M. Harandi, Y. Dai, X. Chang, H. Li, T. Drummond, and Z. Ge, “Hierarchical neural architecture search for deep stereo matching,” Advances in Neural Information Processing Systems , vol. 33, 2020
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R. Gong, W. Liu, Z. Gu, X. Yang, and J. Cheng, “Learning intra-view and cross-view geometric knowledge for stereo matching,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2020
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J. Xing, Z. Qi, J. Dong, J. Cai, and H. Liu, “Mabnet: a lightweight stereo network based on multibranch adjustable bottleneck module,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXVIII 16 . Springer, 2020, pp. 340–356
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J. Zhang, X. Wang, X. Bai, C. Wang, L. Huang, Y. Chen, L. Gu, J. Zhou, T. Harada, and E. R. Hancock, “Revisiting domain generalized stereo matching networks from a feature consistency perspective,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2022, pp. 13 001–13 011
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C. Cai, M. Poggi, S. Mattoccia, and P. Mordohai, “Matching-space stereo networks for cross-domain generalization,” in 2020 International Conference on 3D Vision (3DV) , 2020, pp. 364–373
2020
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J. Watson, O. M. Aodha, D. Turmukhambetov, G. J. Brostow, and M. Firman, “Learning stereo from single images,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part I 16 . Springer, 2020, pp. 722–740
2020
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P. Liu, I. King, M. R. Lyu, and J. Xu, “Flow2stereo: Effective self-supervised learning of optical flow and stereo matching,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2020
2020
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F. Aleotti, F. Tosi, L. Zhang, M. Poggi, and S. Mattoccia, “Reversing the cycle: self-supervised deep stereo through enhanced monocular distillation,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XI 16 . Springer, 2020, pp. 614–632
2020
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R. Liu, C. Yang, W. Sun, X. Wang, and H. Li, “Stereogan: Bridging synthetic-to-real domain gap by joint optimization of domain translation and stereo matching,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2020
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H. Wang, X. Wang, J. Song, J. Lei, and M. Song, “Faster self-adaptive deep stereo,” in Proceedings of the Asian Conference on Computer Vision , 2020
2020
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Y. Zhang, Y. Chen, X. Bai, S. Yu, K. Yu, Z. Li, and K. Yang, “Adaptive unimodal cost volume filtering for deep stereo matching,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 34, no. 07, 2020, pp. 12 926–12 934
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D. Garg, Y. Wang, B. Hariharan, M. Campbell, K. Q. Weinberger, and W.-L. Chao, “Wasserstein distances for stereo disparity estimation,” Advances in Neural Information Processing Systems , vol. 33, pp. 22 517–22 529, 2020
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K. Cheng, T. Wu, and C. Healey, “Revisiting non-parametric matching cost volumes for robust and generalizable stereo matching,” Advances in Neural Information Processing Systems , vol. 35, pp. 16 305–16 318, 2022
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2023
Later among the works it cites.
X. Wang, G. Xu, H. Jia, and X. Yang, “Selective-stereo: Adaptive frequency information selection for stereo matching,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024
2024
Closest in time.
M. Feng, J. Cheng, H. Jia, L. Liu, G. Xu, and X. Yang, “Mc-stereo: Multi-peak lookup and cascade search range for stereo matching,” 2024
2024
Closest in time.
Z. Cheng, J. Yang, and H. Li, “Stereo matching in time: 100+ fps video stereo matching for extended reality,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , 2024, pp. 8719–8728
2024
Closest in time.
Z. Chen, W. Long, H. Yao, Y. Zhang, B. Wang, Y. Qin, and J. Wu, “Mocha-stereo: Motif channel attention network for stereo matching,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024
2024
Closest in time.
Z. Liu, Y. Li, and M. Okutomi, “Global occlusion-aware transformer for robust stereo matching,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , 2024, pp. 3535–3544
2024
Closest in time.
T. Guan, C. Wang, and Y.-H. Liu, “Neural markov random field for stereo matching,” 2024
2024
Closest in time.
X. Li, C. Zhang, W. Su, and W. Tao, “Iinet: Implicit intra-inter information fusion for real-time stereo matching,” Proceedings of the AAAI Conference on Artificial Intelligence , vol. 38, no. 4, pp. 3225–3233, Mar. 2024. [Online]. Available: https://ojs.aaai.org/index.php/AAAI/article/view/28107
2024
Closest in time.
M. Poggi and F. Tosi, “Federated online adaptation for deep stereo,” in CVPR , 2024
2024
Closest in time.
X. Chen, W. Weng, Y. Zhang, and Z. Xiong, “Depth from asymmetric frame-event stereo: A divide-and-conquer approach,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) , January 2024, pp. 3045–3054
2024
Closest in time.
S. Brucker, S. Walz, M. Bijelic, and F. Heide, “Cross-spectral gated-rgb stereo depth estimation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2024
2024
Closest in time.
Z. Liang and C. Li, “Any-stereo: Arbitrary scale disparity estimation for iterative stereo matching,” Proceedings of the AAAI Conference on Artificial Intelligence , vol. 38, no. 4, pp. 3333–3341, Mar. 2024. [Online]. Available: https://ojs.aaai.org/index.php/AAAI/article/view/28119
2024
Closest in time.
2024
Closest in time.
F. Tosi, F. Aleotti, P. Z. Ramirez, M. Poggi, S. Salti, S. Mattoccia, and L. Di Stefano, “Neural disparity refinement,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2024
2024
Closest in time.
2024
Closest in time.
P. Xu, Z. Xiang, C. Qiao, J. Fu, and X. Zhao, “Adaptive multi-modal cross-entropy loss for stereo matching,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024
2024
Closest in time.
L. Yang, B. Kang, Z. Huang, X. Xu, J. Feng, and H. Zhao, “Depth anything: Unleashing the power of large-scale unlabeled data,” in CVPR , 2024
2024
Closest in time.
Y. Liu, J. Ren, J. Zhang, J. Liu, and M. Lin, “Visually imbalanced stereo matching,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 2029–2038
2038
Closest in time.
K. Batsos, C. Cai, and P. Mordohai, “Cbmv: A coalesced bidirectional matching volume for disparity estimation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 2060–2069
2069
Closest in time.