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Dynamic stereo matching is the task of estimating consistent disparities from stereo videos with dynamic objects.
Azuma, R.T.: A survey of augmented reality. Presence: teleoperators & virtual environments 6
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Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. IEEE TPAMI 23
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DeSouza, G.N., Kak, A.C.: Vision for mobile robot navigation: A survey. IEEE transactions on pattern analysis and machine intelligence 24
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Hirschmüller, H., Innocent, P.R., Garibaldi, J.: Real-time correlation-based stereo vision with reduced border errors. IJCV 47
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Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. IJCV 47
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Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. IJCV 47
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Van Meerbergen, G., Vergauwen, M., Pollefeys, M., Van Gool, L.: A hierarchical symmetric stereo algorithm using dynamic programming. IJCV 47
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Hartley, R., Zisserman, A.: Multiple view geometry in computer vision. Cambridge university press (2003)
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Sun, J., Zheng, N.N., Shum, H.Y.: Stereo matching using belief propagation. IEEE TPAMI 25
2003
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Klaus, A., Sormann, M., Karner, K.: Segment-based stereo matching using belief propagation and a self-adapting dissimilarity measure. In: ICPR. vol. 3, pp. 15–18 (2006)
2006
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Yang, Q., Wang, L., Yang, R., Stewénius, H., Nistér, D.: Stereo matching with color-weighted correlation, hierarchical belief propagation, and occlusion handling. IEEE TPAMI 31
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Bleyer, M., Rhemann, C., Rother, C.: Patchmatch stereo-stereo matching with slanted support windows. In: Bmvc. vol. 11, pp. 1–11 (2011)
2011
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Geiger, A., Ziegler, J., Stiller, C.: Stereoscan: Dense 3d reconstruction in real-time. In: 2011 IEEE intelligent vehicles symposium (IV). pp. 963–968. Ieee (2011)
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Butler, D.J., Wulff, J., Stanley, G.B., Black, M.J.: A naturalistic open source movie for optical flow evaluation. In: ECCV. pp. 611–625 (2012)
2012
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Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? the kitti vision benchmark suite. In: CVPR. pp. 3354–3361 (2012)
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2014
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Scharstein, D., Hirschmüller, H., Kitajima, Y., Krathwohl, G., Nešić, N., Wang, X., Westling, P.: High-resolution stereo datasets with subpixel-accurate ground truth. In: German Conference on Pattern Recognition. pp. 31–42 (2014)
2014
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Menze, M., Geiger, A.: Object scene flow for autonomous vehicles. In: CVPR. pp. 3061–3070 (2015)
2015
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Zbontar, J., LeCun, Y.: Computing the stereo matching cost with a convolutional neural network. In: CVPR. pp. 1592–1599 (2015)
2015
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Mayer, N., Ilg, E., Hausser, P., Fischer, P., Cremers, D., Dosovitskiy, A., Brox, T.: A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation. In: CVPR. pp. 4040–4048 (2016)
2016
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Kendall, A., Martirosyan, H., Dasgupta, S., Henry, P., Kennedy, R., Bachrach, A., Bry, A.: End-to-end learning of geometry and context for deep stereo regression. In: CVPR. pp. 66–75 (2017)
2017
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Pang, J., Sun, W., Ren, J.S., Yang, C., Yan, Q.: Cascade residual learning: A two-stage convolutional neural network for stereo matching. In: CVPRW. pp. 887–895 (2017)
Xu, H., Zhang, J.: Aanet: Adaptive aggregation network for efficient stereo matching. In: CVPR. pp. 1959–1968 (2020)
2020
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2021
Later among the works it cites.
2021
Later among the works it cites.
Shen, Z., Dai, Y., Rao, Z.: Cfnet: Cascade and fused cost volume for robust stereo matching. In: CVPR. pp. 13906–13915 (2021)
2021
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Song, X., Yang, G., Zhu, X., Zhou, H., Wang, Z., Shi, J.: Adastereo: a simple and efficient approach for adaptive stereo matching. In: CVPR. pp. 10328–10337 (2021)
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2017
Cited alongside, same era.
Schops, T., Schonberger, J.L., Galliani, S., Sattler, T., Schindler, K., Pollefeys, M., Geiger, A.: A multi-view stereo benchmark with high-resolution images and multi-camera videos. In: CVPR. pp. 3260–3269 (2017)
2017
Cited alongside, same era.
2017
Cited alongside, same era.
Chang, J.R., Chen, Y.S.: Pyramid stereo matching network. In: CVPR. pp. 5410–5418 (2018)
2018
Cited alongside, same era.
Pang, J., Sun, W., Yang, C., Ren, J., Xiao, R., Zeng, J., Lin, L.: Zoom and learn: Generalizing deep stereo matching to novel domains. In: CVPR. pp. 2070–2079 (2018)
2018
Cited alongside, same era.
Tremblay, J., To, T., Birchfield, S.: Falling things: A synthetic dataset for 3d object detection and pose estimation. In: CVPRW. pp. 2038–2041 (2018)
2018
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Yao, Y., Luo, Z., Li, S., Fang, T., Quan, L.: Mvsnet: Depth inference for unstructured multi-view stereo. In: Proceedings of the European conference on computer vision (ECCV). pp. 767–783 (2018)
2018
Cited alongside, same era.
Zhong, Y., Li, H., Dai, Y.: Open-world stereo video matching with deep rnn. In: Proceedings of the European Conference on Computer Vision (ECCV). pp. 101–116 (2018)
2018
Cited alongside, same era.
2021
Later among the works it cites.
Tankovich, V., Hane, C., Zhang, Y., Kowdle, A., Fanello, S., Bouaziz, S.: Hitnet: Hierarchical iterative tile refinement network for real-time stereo matching. In: CVPR. pp. 14362–14372 (2021)
2021
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Teed, Z., Deng, J.: Raft-3d: Scene flow using rigid-motion embeddings. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 8375–8384 (2021)
2021
Later among the works it cites.
Li, J., Wang, P., Xiong, P., Cai, T., Yan, Z., Yang, L., Liu, J., Fan, H., Liu, S.: Practical stereo matching via cascaded recurrent network with adaptive correlation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 16263–16272 (2022)
2022
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Liu, H., Ruan, Z., Zhao, P., Dong, C., Shang, F., Liu, Y., Yang, L., Timofte, R.: Video super-resolution based on deep learning: a comprehensive survey. Artificial Intelligence Review 55
2022
Later among the works it cites.
2022
Later among the works it cites.
Chang, T., Yang, X., Zhang, T., Wang, M.: Domain generalized stereo matching via hierarchical visual transformation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 9559–9568 (2023)
2023
Later among the works it cites.
Jing, J., Li, J., Xiong, P., Liu, J., Liu, S., Guo, Y., Deng, X., Xu, M., Jiang, L., Sigal, L.: Uncertainty guided adaptive warping for robust and efficient stereo matching. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). pp. 3318–3327 (October 2023)
2023
Later among the works it cites.
Karaev, N., Rocco, I., Graham, B., Neverova, N., Vedaldi, A., Rupprecht, C.: Dynamicstereo: Consistent dynamic depth from stereo videos. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 13229–13239 (2023)
2023
Later among the works it cites.
Li, Z., Ye, W., Wang, D., Creighton, F.X., Taylor, R.H., Venkatesh, G., Unberath, M.: Temporally consistent online depth estimation in dynamic scenes. In: Proceedings of the IEEE/CVF winter conference on applications of computer vision. pp. 3018–3027 (2023)
2023
Later among the works it cites.
Rao, Z., Xiong, B., He, M., Dai, Y., He, R., Shen, Z., Li, X.: Masked representation learning for domain generalized stereo matching. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 5435–5444 (2023)
2023
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Xu, G., Wang, X., Ding, X., Yang, X.: Iterative geometry encoding volume for stereo matching. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 21919–21928 (2023)
2023
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Zhang, Y., Poggi, M., Mattoccia, S.: Temporalstereo: Efficient spatial-temporal stereo matching network. In: 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). pp. 9528–9535. IEEE (2023)
2023
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Cheng, Z., Yang, J., Li, H.: 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. pp. 8719–8728 (2024)
2024
Closest in time.