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Recent convolutional neural networks, especially end-to-end disparity estimation models, achieve remarkable performance on stereo matching task.
A taxonomy and evaluation of dense two-frame stereo correspondence algorithms
Scharstein, D., Szeliski, R.: · 2002
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Accurate and efficient stereo processing by semi-global matching and mutual information
Hirschmuller, H.: · 2005
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Estimating optimal parameters for mrf stereo from a single image pair
Zhang, L., Seitz, S.M.: · 2007
Earlier work this paper cites.
Stereo vision and laser odometry for autonomous helicopters in gps-denied indoor environments
Achtelik, M., Bachrach, A., He, R., Prentice, S., Roy, N.: · 2009
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Curriculum learning
Bengio, Y., Louradour, J., Collobert, R., Weston, J.: · 2009
Earlier work this paper cites.
Object stereo¡ªjoint stereo matching and object segmentation
Bleyer, M., Rother, C., Kohli, P., Scharstein, D., Sinha, S.: · 2011
Earlier work this paper cites.
Contour detection and hierarchical image segmentation
Arbelaez, P., Maire, M., Fowlkes, C., Malik, J.: · 2011
Earlier work this paper cites.
Are we ready for autonomous driving? the kitti vision benchmark suite
Geiger, A., Lenz, P., Urtasun, R.: · 2012
Earlier work this paper cites.
Stereo vision based indoor/outdoor navigation for flying robots
Schmid, K., Tomic, T., Ruess, F., Hirschmüller, H., Suppa, M.: · 2013
Earlier work this paper cites.
Efficient joint segmentation, occlusion labeling, stereo and flow estimation
Yamaguchi, K., McAllester, D., Urtasun, R.: · 2014
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The role of context for object detection and semantic segmentation in the wild
Mottaghi, R., Chen, X., Liu, X., Cho, N.G., Lee, S.W.: · 2014
Earlier work this paper cites.
Caffe: Convolutional architecture for fast feature embedding
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D.P., Ba, J.: · 2014
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Flownet: Learning optical flow with convolutional networks
Dosovitskiy, A., Fischery, P., Ilg, E., HUsser, P.: · 2015
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Displets: Resolving stereo ambiguities using object knowledge
Guney, F., Geiger, A.: · 2015
Cited alongside, same era.
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., Darrell, T.: · 2015
Cited alongside, same era.
Holistically-nested edge detection
Xie, S., Tu, Z.: · 2015
Cited alongside, same era.
Flownet: Learning optical flow with convolutional networks
Dosovitskiy, A., Fischer, P., Ilg, E., Hausser, P., Hazirbas, C.: · 2015
Cited alongside, same era.
Very deep convolutional networks for large-scale image recognition
Simonyan, K., Zisserman, A.: · 2015
Cited alongside, same era.
Object scene flow for autonomous vehicles
Menze, M., Geiger, A.: · 2015
Cited alongside, same era.
Cascade residual learning: A two-stage convolutional neural network for stereo matching
Pang, J., Sun, W., Ren, J., Yang, C., Yan, Q.: · 2017
Later among the works it cites.
Improved stereo matching with constant highway networks and reflective confidence learning
Shaked, A., Wolf, L.: · 2017
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Detect, replace, refine: Deep structured prediction for pixel wise labeling
Gidaris, S., Komodakis, N.: · 2017
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Self-supervised learning for stereo matching with self-improving ability
Zhong, Y., Dai, Y., Li, H.: · 2017
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Learning deep correspondence through prior and posterior feature constancy
Liang, Z., Feng, Y., Guo, Y., Liu, H.: · 2017
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Stereo matching by training a convolutional neural network to compare image patches
Zbontar, J., LeCun, Y.: · 2016
Cited alongside, same era.
A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation
Mayer, N., Ilg, E., Hausser, P., Fischer, P., Cremers, D., Dosovitskiy, A., Brox, T.: · 2016
Cited alongside, same era.
Efficient deep learning for stereo matching
Luo, W., Schwing, A.G., Urtasun, R.: · 2016
Cited alongside, same era.
Parsenet: Looking wider to see better
Liu, W., Rabinovich, A., Berg, A.C.: · 2016
Cited alongside, same era.
Learning relaxed deep supervision for better edge detection
Liu, Y., Lew, M.S.: · 2016
Cited alongside, same era.
Patch based confidence prediction for dense disparity map
Seki, A., Pollefeys, M.: · 2016
Cited alongside, same era.
Liu, Y., Cheng, M.M., Hu, X., Wang, K., Bai, X.: · 2017
Later among the works it cites.
SegFlow: Joint learning for video object segmentation and optical flow
Cheng, J., Tsai, Y.H., Wang, S., Yang, M.H.: · 2017
Later among the works it cites.
Pyramid scene parsing network
Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: · 2017
Later among the works it cites.
Unsupervised monocular depth estimation with left-right consistency
Godard, C., Aodha, O.M., Brostow, G.J.: · 2017
Later among the works it cites.
SGM-Nets: Semi-global matching with neural networks
Seki, A., Pollefeys, M.: · 2017
Later among the works it cites.
Deep stereo matching with explicit cost aggregation sub-architecture
Yu, L., Wang, Y., Wu, Y., Jia, Y.: · 2018
Closest in time.
Pyramid stereo matching network
Chang, J.R., Chen, Y.S.: · 2018
Closest in time.
Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs
Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: · 2018
Closest in time.