Fetching the paper…
Reading the bibliography…
Current self-supervised methods for monocular depth estimation are largely based on deeply nested convolutional networks that leverage stereo image pairs or monocular sequences during a training phase.
1904
Earlier work this paper cites.
D. Scharstein and R. Szeliski, “A taxonomy and evaluation of dense two-frame stereo correspondence algorithms,” in IEEE Int. J. Comput. Vis. , vol. 47, pp. 7–42, 2002
2002
Earlier work this paper cites.
D. Hoiem, AA. Efros, and M. Hebert, “Geometric context from a single image,” in Int. Conf. on Comput. Vis. , pp. 654–661, 2005
2005
Earlier work this paper cites.
DC. Lee, M. Hebert, and T. Kanade, “Geometric reasoning for single image structure recovery,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2009
2009
Earlier work this paper cites.
A. Saxena, M. Sun, and A. Y. Ng, “Make3D: Learning 3D scene structure from a single still image,” in IEEE Trans. Pattern Anal. Mach. Intell., vol. 31, no. 5, pp. 824–840, May 2009
2009
Earlier work this paper cites.
G. J. Brostow, J. Fauqueur, and R. Cipolla, “Semantic object classes in video: A high-definition ground truth database,” in Pattern Recognit. Letters, 2009
2009
Earlier work this paper cites.
N. Silberman, P. Kohli, D. Hoiem, and R. Fergus, “Indoor segmentation and support inference from RGBD images,” in Proc. Eur. Conf. Comput. Vis. , pp. 746–760, 2012
2012
Earlier work this paper cites.
A. Geiger, P. Lenz, and R. Urtasun, “Are we ready for autonomous driving? the KITTI vision benchmark suite,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2012
2012
Earlier work this paper cites.
D. Eigen, C. Puhrsch, and R. Fergus, “Depth map prediction from a single image using a multi-scale deep network,” in Proc. Advances in Neural Inf. Process. Syst. , pp. 2366–2374, 2014
2014
Earlier work this paper cites.
J. Ba, and R. Caruana, “Do deep nets really need to be deep,” in Proc. Advances in Neural Inf. Process. Syst. , pp. 2654–2662, 2014
2014
Earlier work this paper cites.
K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition?,” in arXiv. , 2014
2014
Earlier work this paper cites.
D. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in arXiv. , 2014
2014
Earlier work this paper cites.
T. Cao, Z.-Y. Xiang, and J.-L. Liu, “Perception in disparity: An efficient navigation framework for autonomous vehicles with stereo cameras,” in IEEE Intell. Transp. Syst. , 2015
2015
Earlier work this paper cites.
S. Gupta, P. Arbelaez, R. Girshick, and J. Malik, “Indoor scene understanding with rgb-d images: Bottom-up segmentation, object detection and semantic segmentation,” in IEEE Int. J. Comput. Vis. , vol. 112, no. 2, pp. 133–149, 2015
2015
Earlier work this paper cites.
X. Han, T. Leung, Y. Jia, and R. Sukthankar, “Matchnet: Unifying feature and metric learning for patch-based matching,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , pp. 3279–3286, 2015
2015
Earlier work this paper cites.
J. Zbontar, Y. LeCun, “Computing the stereo matching cost with a convolutional neural network,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , pp. 1592–1599, 2015
2015
Earlier work this paper cites.
F. Liu, C. Shen, and G. Lin, “Deep convolutional neural fields for depth estimation from a single image,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , pp. 5162–5170, 2015
2015
Earlier work this paper cites.
G. Hinton, O. Vinyals, and J. Dean, “Distilling the knowledge in a neural network,” in arXiv. , 2015
2015
Earlier work this paper cites.
O. Russakovsky et al., “ImageNet Large scale visual recognition challenge,” in Int. Journ. Comput. Vis. , vol. 115, no. 3, pp. 211–252, 2015
2015
Earlier work this paper cites.
M. Park and K. Yoon, “Leveraging stereo matching with learning-based confidence measures,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2015
2015
Earlier work this paper cites.
J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , pp. 3431–3440, 2015
2015
Earlier work this paper cites.
O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in Int. Conf. on Medical Image Computing and computer-assisted intervention. , 2015
2015
Earlier work this paper cites.
A. Vedaldi, and K. Lnc, “Matconvnet: Convolutional neural networks for matlab,” in Proc. ACM Int. Conf. Multimedia , pp. 689–692, 2015
2015
Cited alongside, same era.
S. Kim, K. Park, K. Sohn, and S. Lin, “Unified depth prediction and intrinsic image decomposition from a single image via joint convolutional neural fields,” in Eur. Conf. on Comput. Vis. , pp. 143–159, 2016
2016
Cited alongside, same era.
W. Luo, AG. Schwing, and R. Urtasun, “Efficient deep learning for stereo matching,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , pp. 5695–5703, 2016
2016
Cited alongside, same era.
N. Mayer, E. Ilg, P. Hausser, P. Fischer, D. Cremers, A. Dosovitskiy, and T. Brox, “A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , pp. 4040–4048, 2016
2016
Cited alongside, same era.
Velodyne. Accessed: Feb. 15, 2017. [ [ Online ] ] . Available: http://velodynelidar.com/
2017
Later among the works it cites.
B. Yang, S. Rosa, A. Markham, N. Trigoni, and H. Wen, “Dense 3D object reconstruction from a single depth view,” in IEEE Trans. Pattern Anal. Mach. Intell. , 2018
2018
Later among the works it cites.
Y. Choi et al., “KAIST multi-spectral day/night data set for autonomous and assisted driving,”in IEEE Intell. Transp. Syst. , 2018
2018
Later among the works it cites.
J. Cho, Y. Kim, H. Jung, C. Oh, J. Youn, and K. Sohn, “Multi-task self-supervised visual representation learning for monocular road segmentation,” in IEEE Int. Conf. on Multi. and Expo. , 2018
2018
Later among the works it cites.
JR. Chang, and YS. Chen, “Pyramid stereo matching network,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , pp. 5410–5418, 2018
2018
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
C. Godard, O. Mac Aodha, and G. J. Brostow, “Unsupervised monocular depth estimation with left-right consistency,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2016
2016
Cited alongside, same era.
M. Cordts, M. Omran, S. Ramos, and T. Rehfeld, “The Cityscapes Dataset for Semantic Urban Scene Understanding,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , pp. 3213–3223, 2016
2016
Cited alongside, same era.
R. Garg, VK. BG, G. Carneiro, and I. Reid, “Unsupervised cnn for single view depth estimation: Geometry to the rescue,” in Proc. Eur. Conf. Comput. Vis. , pp. 740–756, 2016
2016
Cited alongside, same era.
J. Xie, R. Girshick, and A. Farhadi, “Deep3d: Fully automatic 2d-to-3d video conversion with deep convolutional neural networks,” in Proc. Eur. Conf. Comput. Vis. , pp. 842–857, 2016
2016
Cited alongside, same era.
M. Noroozi, and P. Favaro, “Unsupervised learning of visual representations by solving jigsaw puzzles,” in Eur. Conf. on Comput. Vis. , pp. 69–84, 2016
2016
Cited alongside, same era.
D. Pathak, P. Krahenbuhl, and J. Donahue, “Context encoders: Feature learning by inpainting,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , pp. 2536–2544, 2016
2016
Cited alongside, same era.
M. Poggi, and S. Mattoccia, “Learning from scratch a confidence measure,” in Proc. Brit. Mach. Vis. Conf. , vol. 10, pp. 1–13, 2016
2016
Cited alongside, same era.
K. He, X. Zhnag, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , pp. 770–778, 2016
2016
Cited alongside, same era.
Later among the works it cites.
Y. Kim, H. Jung, D. Min, and K. Sohn, “Deep Monocular Depth Estimation via Integration of Global and Local Predictions,” IEEE Trans. Image Process. , vol. 27, no. 8, pp. 4131–4144, 2018
2018
Later among the works it cites.
Y. Luo, J. Ren, M. Lin, J. Pang, and W. Sun, “Single view stereo matching,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , pp. 155–163, 2018
2018
Later among the works it cites.
X. Guo, H. Li, S. Yi, J. Ren, and X. Wang, “Learning monocular depth by distilling cross-domain stereo networks,” in Proc. Eur. Conf. Comput. Vis. , 2018
2018
Later among the works it cites.
H. Fu, M. Gong, C. Wang, K. Batmanghelich, and D. Tao, “Deep ordinal regression network for monocular depth estimation,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2018
2018
Later among the works it cites.
Z. Xu, YC. Hsu, and J. Huang, “Training Student Networks for Acceleration with Conditional Adversarial Networks,” in Proc. Brit. Mach. Vis. Conf. , 2018
2018
Later among the works it cites.
I. Radosavovic, P. Dollár, R. Girshick, G. Gkioxari, and K. He, “Data distillation: Towards omni-supervised learning,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2018
2018
Later among the works it cites.
Y. Ding, L. Wang, D. Fan, and B. Gong , ‘A semi-supervised two-stage approach to learning from noisy labels,” in Proc. IEEE Winter Conf. Appli. Comput. Vis. , 2018
2018
Later among the works it cites.
H. Jiang, G. Larsson, M. Maire Greg Shakhnarovich and E. Learned-Miller, “Self-supervised relative depth learning for urban scene understanding,” in Proc. Eur. Conf. Comput. Vis. , 2018
2018
Later among the works it cites.
C Godard, O.M. Aodha, M. Firman, and G.J. Brostow, “Digging into self-supervised monocular depth estimation,” in Proc. Int. Conf. Comput. Vis. , 2019
2019
Closest in time.
F. Tosi, F. Aleotti, M. Poggi, and S. Mattoccia, “Learning monocular depth estimation infusing traditional stereo knowledge,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2019
2019
Closest in time.
S. Zhao, H. Fu, M. Gong, and D. Tao, “Geometry-aware symmetric domain adaptation for monocular depth estimation,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2019
2019
Closest in time.
K. Lasinger, R. Ranftl, K. Schindler, and V. Koltun , “Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer,” in arXiv , 2019
2019
Closest in time.
J. Ye, Y. Ji, X. Wang, K. Ou, D. Tao, and M. Song “Student Becoming the Master: Knowledge Amalgamation for Joint Scene Parsing, Depth Estimation, and More,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2019
2019
Closest in time.
A. Pilzer, S. Lathuiliere, N. Sebe, and E. Ricci, “Refine and distill: Exploiting cycle-inconsistency and knowledge distillation for unsupervised monocular depth estimation,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2019
2019
Closest in time.
J. Li, Y. Wong, Q. Zhao, and M.S. Kankanhalli, ‘Learning to learn from noisy labeled data,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2019
2019
Closest in time.
F. Zhang, V. Prisacariu, R. Yang, and P.HS. Torr, “Ga-net: Guided aggregation net for end-to-end stereo matching,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2019
2019
Closest in time.