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Stereo-matching is a fundamental problem in computer vision.
1905
Earlier work this paper cites.
Hirschmuller, H.: Stereo processing by semiglobal matching and mutual information. IEEE Transactions on pattern analysis and machine intelligence 30
2007
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Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Asian conference on computer vision. pp. 25–38. Springer (2010)
2010
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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: European conference on computer vision. pp. 611–625. Springer (2012)
2012
Earlier work this paper cites.
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. Springer (2014)
2014
Earlier work this paper cites.
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: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 3061–3070 (2015)
2015
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2016
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Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: CARLA: An open urban driving simulator. In: Proceedings of the 1st Annual Conference on Robot Learning. pp. 1–16 (2017)
2017
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Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., Lerer, A.: Automatic differentiation in pytorch (2017)
2017
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Schöps, T., Schönberger, 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: Conference on Computer Vision and Pattern Recognition (CVPR) (2017)
2017
Cited alongside, same era.
2017
Cited alongside, same era.
Tremblay, J., To, T., Birchfield, S.: Falling things: A synthetic dataset for 3d object detection and pose estimation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. pp. 2038–2041 (2018)
2018
Cited alongside, same era.
Cheng, X., Wang, P., Yang, R.: Learning depth with convolutional spatial propagation network. IEEE transactions on pattern analysis and machine intelligence 42
2019
Cited alongside, same era.
Teed, Z., Deng, J.: Raft: Recurrent all-pairs field transforms for optical flow. In: European conference on computer vision. pp. 402–419. Springer (2020)
2020
Later among the works it cites.
Wang, W., Zhu, D., Wang, X., Hu, Y., Qiu, Y., Wang, C., Hu, Y., Kapoor, A., Scherer, S.: Tartanair: A dataset to push the limits of visual slam (2020)
2020
Later among the works it cites.
Zhang, Y., Chen, Y., Bai, X., Yu, S., Yu, K., Li, Z., Yang, K.: Adaptive unimodal cost volume filtering for deep stereo matching. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 34, pp. 12926–12934 (2020)
2020
Later among the works it cites.
Blender Online Community: Blender - a 3D modelling and rendering package. Blender Foundation, Blender Institute, Amsterdam (2021)
2021
Later among the works it cites.
Jiang, H., Ding, L., Sun, Z., Huang, R.: Unsupervised monocular depth perception: Focusing on moving objects. IEEE Sensors Journal (2021)
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2019
Cited alongside, same era.
Yang, G., Manela, J., Happold, M., Ramanan, D.: Hierarchical deep stereo matching on high-resolution images. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (June 2019)
2019
Cited alongside, same era.
Zhang, F., Prisacariu, V., Yang, R., Torr, P.H.: Ga-net: Guided aggregation net for end-to-end stereo matching. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 185–194 (2019)
2019
Cited alongside, same era.
Bao, W., Wang, W., Xu, Y., Guo, Y., Hong, S., Zhang, X.: Instereo2k: A large real dataset for stereo matching in indoor scenes. Science China Information Sciences 63
2020
Cited alongside, same era.
Cheng, X., Zhong, Y., Harandi, M., Dai, Y., Chang, X., Li, H., Drummond, T., Ge, Z.: Hierarchical neural architecture search for deep stereo matching. Advances in Neural Information Processing Systems 33
2020
Cited alongside, same era.
Ranftl, R., Lasinger, K., Hafner, D., Schindler, K., Koltun, V.: Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer. IEEE transactions on pattern analysis and machine intelligence (2020)
2020
Cited alongside, same era.
2021
Later among the works it cites.
Lipson, L., Teed, Z., Deng, J.: Raft-stereo: Multilevel recurrent field transforms for stereo matching. In: 2021 International Conference on 3D Vision (3DV). pp. 218–227. IEEE (2021)
2021
Later among the works it cites.
Wang, H., Fan, R., Cai, P., Liu, M.: Pvstereo: Pyramid voting module for end-to-end self-supervised stereo matching (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
Closest in time.
Xu, G., Cheng, J., Guo, P., Yang, X.: Attention concatenation volume for accurate and efficient stereo matching. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 12981–12990 (2022)
2022
Closest in time.