Fetching the paper…
Reading the bibliography…
Disparity estimation for binocular stereo images finds a wide range of applications.
Kong, D., Tao, H.: A method for learning matching errors for stereo computation. In: BMVC (2004)
2004
Earlier work this paper cites.
Hirschmuller, H.: Stereo processing by semiglobal matching and mutual information. TPAMI (2008)
2008
Earlier work this paper cites.
Hirschmuller, H., Scharstein, D.: Evaluation of stereo matching costs on images with radiometric differences. TPAMI (2009)
2009
Earlier work this paper cites.
Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: ACCV (2010)
2010
Earlier work this paper cites.
Brown, M., Hua, G., Winder, S.: Discriminative learning of local image descriptors. TPAMI (2011)
2011
Earlier work this paper cites.
Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? the kitti vision benchmark suite. In: CVPR (2012)
2012
Earlier work this paper cites.
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R.B., Guadarrama, S., Darrell, T.: Caffe: Convolutional architecture for fast feature embedding. In: ACM MM (2014)
2014
Earlier work this paper cites.
Yamaguchi, K., McAllester, D., Urtasun, R.: Efficient joint segmentation, occlusion labeling, stereo and flow estimation. In: ECCV (2014)
2014
Earlier work this paper cites.
Chen, L., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Semantic image segmentation with deep convolutional nets and fully connected crfs (2015)
2015
Earlier work this paper cites.
Chen, Z., Sun, X., Wang, L., Yu, Y., Huang, C.: A deep visual correspondence embedding model for stereo matching costs. In: ICCV (2015)
2015
Earlier work this paper cites.
Dosovitskiy, A., Fischer, P., Ilg, E., Hausser, P., Hazirbas, C., Golkov, V., van der Smagt, P., Cremers, D., Brox, T.: Flownet: Learning optical flow with convolutional networks. In: ICCV (2015)
2015
Earlier work this paper cites.
Guney, F., Geiger, A.: Displets: Resolving stereo ambiguities using object knowledge. In: CVPR (2015)
2015
Earlier work this paper cites.
Heise, P., Jensen, B., Klose, S., Knoll, A.: Fast dense stereo correspondences by binary locality sensitive hashing. In: ICRA (2015)
2015
Earlier work this paper cites.
Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: CVPR (2015)
2015
Earlier work this paper cites.
Menze, M., Geiger, A.: Object scene flow for autonomous vehicles. In: CVPR (2015)
2015
Earlier work this paper cites.
Bai, M., Luo, W., Kundu, K., Urtasun, R.: Exploiting semantic information and deep matching for optical flow. In: ECCV (2016)
2016
Cited alongside, same era.
Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., Schiele, B.: The cityscapes dataset for semantic urban scene understanding. In: CVPR (2016)
2016
Cited alongside, same era.
Flynn, J., Neulander, I., Philbin, J., Snavely, N.: Deepstereo: Learning to predict new views from the world’s imagery. In: CVPR (2016)
2016
Cited alongside, same era.
Garg, R., Carneiro, G., Reid, I.: Unsupervised cnn for single view depth estimation: Geometry to the rescue. In: ECCV (2016)
2016
Cited alongside, same era.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)
2016
Cited alongside, same era.
Cheng, J., Tsai, Y.H., Wang, S., Yang, M.H.: Segflow: Joint learning for video object segmentation and optical flow. In: ICCV (2017)
2017
Later among the works it cites.
Gidaris, S., Komodakis, N.: Detect, replace, refine: Deep structured prediction for pixel wise labeling. In: CVPR (2017)
2017
Later among the works it cites.
Godard, C., Mac Aodha, O., Brostow, G.J.: Unsupervised monocular depth estimation with left-right consistency. In: CVPR (2017)
2017
Later among the works it cites.
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: ICCV (2017)
2017
Later among the works it cites.
Kuznietsov, Y., Stückler, J., Leibe, B.: Semi-supervised deep learning for monocular depth map prediction. In: CVPR (2017)
2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Jason, J.Y., Harley, A.W., Derpanis, K.G.: Back to basics: Unsupervised learning of optical flow via brightness constancy and motion smoothness. In: ECCV Workshop (2016)
2016
Cited alongside, same era.
Luo, W., Schwing, A.G., Urtasun, R.: Efficient deep learning for stereo matching. In: CVPR (2016)
2016
Cited alongside, same era.
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 (2016)
2016
Cited alongside, same era.
Revaud, J., Weinzaepfel, P., Harchaoui, Z., Schmid, C.: Deepmatching: Hierarchical deformable dense matching. IJCV (2016)
2016
Cited alongside, same era.
Seki, A., Pollefeys, M.: Patch based confidence prediction for dense disparity map. In: BMVC (2016)
2016
Cited alongside, same era.
Xie, J., Girshick, R., Farhadi, A.: Deep3d: Fully automatic 2d-to-3d video conversion with deep convolutional neural networks. In: ECCV (2016)
2016
Cited alongside, same era.
Zbontar, J., LeCun, Y.: Stereo matching by training a convolutional neural network to compare image patches. JMLR (2016)
2016
Cited alongside, same era.
Pang, J., Sun, W., Ren, J., Yang, C., Yan, Q.: Cascade residual learning: A two-stage convolutional neural network for stereo matching. In: ICCV Workshop (2017)
2017
Later among the works it cites.
Ren, Z., Sun, D., Kautz, J., Sudderth, E.B.: Cascaded scene flow prediction using semantic segmentation. In: ICCV Workshop (2017)
2017
Later among the works it cites.
Shaked, A., Wolf, L.: Improved stereo matching with constant highway networks and reflective confidence learning. In: CVPR (2017)
2017
Later among the works it cites.
Vijay, B., Alex, K., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. TPAMI (2017)
2017
Later among the works it cites.
Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: CVPR (2017)
2017
Later among the works it cites.
Zhou, C., Zhang, H., Shen, X., Jia, J.: Unsupervised learning of stereo matching. In: ICCV (2017)
2017
Later among the works it cites.
Chang, J., Chen, Y.: Pyramid stereo matching network. In: CVPR (2018)
2018
Closest in time.
Liang, Z., Feng, Y., Guo, Y., Liu, H., Chen, W., Qiao, L., Z., L., Z., J.: Learning for disparity estimation through feature constancy. In: CVPR (2018)
2018
Closest in time.
Meister, S., Hur, J., Roth, S.: Unflow: Unsupervised learning of optical flow with a bidirectional census loss. In: AAAI (2018)
2018
Closest in time.
Yu, L., Wang, Y., Wu, Y., Jia, Y.: Deep stereo matching with explicit cost aggregation sub-architecture. In: AAAI (2018)
2018
Closest in time.