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We present a novel unsupervised learning framework for single view depth estimation using monocular videos.
Image quality assessment: from error visibility to structural similarity
Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli · 2004
Earlier work this paper cites.
Learning depth from single monocular images
A. Saxena, S. H. Chung, and A. Y. Ng · 2006
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Make3d: Learning 3d scene structure from a single still image
A. Saxena, M. Sun, and A. Y. Ng · 2009
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Single image depth estimation from predicted semantic labels
B. Liu, S. Gould, and D. Koller · 2010
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Are we ready for autonomous driving? the kitti vision benchmark suite
A. Geiger, P. Lenz, and R. Urtasun · 2012
Earlier work this paper cites.
Depth map prediction from a single image using a multi-scale deep network
D. Eigen, C. Puhrsch, and R. Fergus · 2014
Earlier work this paper cites.
Lsd-slam: Large-scale direct monocular slam
J. Engel, T. Schöps, and D. Cremers · 2014
Earlier work this paper cites.
Depth transfer: Depth extraction from video using non-parametric sampling
K. Karsch, C. Liu, and S. B. Kang · 2014
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Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
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Pulling things out of perspective
L. Ladicky, J. Shi, and M. Pollefeys · 2014
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Discrete-continuous depth estimation from a single image
M. Liu, M. Salzmann, and X. He · 2014
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
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Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture
D. Eigen and R. Fergus · 2015
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Spatial transformer networks
M. Jaderberg, K. Simonyan, A. Zisserman, et al · 2015
Earlier work this paper cites.
Depth and surface normal estimation from monocular images using regression on deep features and hierarchical crfs
B. Li, C. Shen, Y. Dai, A. Van Den Hengel, and M. He · 2015
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Deep convolutional neural fields for depth estimation from a single image
F. Liu, C. Shen, and G. Lin · 2015
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Object scene flow for autonomous vehicles
M. Menze and A. Geiger · 2015
Earlier work this paper cites.
Unsupervised representation learning with deep convolutional generative adversarial networks
A. Radford, L. Metz, and S. Chintala · 2015
Earlier work this paper cites.
Towards unified depth and semantic prediction from a single image
P. Wang, X. Shen, Z. Lin, S. Cohen, B. Price, and A. L. Yuille · 2015
Earlier work this paper cites.
Learning ordinal relationships for mid-level vision
D. Zoran, P. Isola, D. Krishnan, and W. T. Freeman · 2015
Earlier work this paper cites.
Single-image depth perception in the wild
W. Chen, Z. Fu, D. Yang, and J. Deng · 2016
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The cityscapes dataset for semantic urban scene understanding
M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele · 2016
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Deepstereo: Learning to predict new views from the world’s imagery
J. Flynn, I. Neulander, J. Philbin, and N. Snavely · 2016
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Unsupervised cnn for single view depth estimation: Geometry to the rescue
R. Garg, V. K. BG, G. Carneiro, and I. Reid · 2016
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Deeper depth prediction with fully convolutional residual networks
I. Laina, C. Rupprecht, V. Belagiannis, F. Tombari, and N. Navab · 2016
Direct sparse odometry
J. Engel, V. Koltun, and D. Cremers · 2018
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Digging into self-supervised monocular depth estimation
C. Godard, O. Mac Aodha, and G. Brostow · 2018
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Learning monocular depth by distilling cross-domain stereo networks
X. Guo, H. Li, S. Yi, J. Ren, and X. Wang · 2018
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Depthnet: A recurrent neural network architecture for monocular depth prediction
A. C. Kumar, S. M. Bhandarkar, and P. Mukta · 2018
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Monocular depth prediction using generative adversarial networks
A. C. Kumar, S. M. Bhandarkar, and P. Mukta · 2018
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Fast and accurate image super-resolution with deep laplacian pyramid networks
W.-S. Lai, J.-B. Huang, N. Ahuja, and M.-H. Yang · 2018
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Learning depth from single monocular images using deep convolutional neural fields
F. Liu, C. Shen, G. Lin, and I. D. Reid · 2016
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Structure-from-motion revisited
J. L. Schonberger and J.-M. Frahm · 2016
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Pixelwise view selection for unstructured multi-view stereo
J. L. Schönberger, E. Zheng, J.-M. Frahm, and M. Pollefeys · 2016
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Deep3d: Fully automatic 2d-to-3d video conversion with deep convolutional neural networks
J. Xie, R. Girshick, and A. Farhadi · 2016
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Estimating depth from monocular images as classification using deep fully convolutional residual networks
Y. Cao, Z. Wu, and C. Shen · 2017
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Unsupervised monocular depth estimation with left-right consistency
C. Godard, O. Mac Aodha, and G. J. Brostow · 2017
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Megadepth: Learning single-view depth prediction from internet photos
Z. Li and N. Snavely · 2018
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Unsupervised learning of depth and ego-motion from monocular video using 3d geometric constraints
R. Mahjourian, M. Wicke, and A. Angelova · 2018
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Structured adversarial training for unsupervised monocular depth estimation
I. Mehta, P. Sakurikar, and P. Narayanan · 2018
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Unsupervised adversarial depth estimation using cycled generative networks
A. Pilzer, D. Xu, M. Puscas, E. Ricci, and N. Sebe · 2018
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Towards real-time unsupervised monocular depth estimation on cpu
M. Poggi, F. Aleotti, F. Tosi, and S. Mattoccia · 2018
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Learning monocular depth estimation with unsupervised trinocular assumptions
M. Poggi, F. Tosi, and S. Mattoccia · 2018
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Learning depth from monocular videos using direct methods
C. Wang, J. M. Buenaposada, R. Zhu, and S. Lucey · 2018
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Recovering realistic texture in image super-resolution by deep spatial feature transform
X. Wang, K. Yu, C. Dong, and C. C. Loy · 2018
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Every pixel counts: Unsupervised geometry learning with holistic 3d motion understanding
Z. Yang, P. Wang, Y. Wang, W. Xu, and R. Nevatia · 2018
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Lego: Learning edge with geometry all at once by watching videos
Z. Yang, P. Wang, Y. Wang, W. Xu, and R. Nevatia · 2018
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Unsupervised learning of geometry from videos with edge-aware depth-normal consistency
Z. Yang, P. Wang, W. Xu, L. Zhao, and R. Nevatia · 2018
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Geonet: Unsupervised learning of dense depth, optical flow and camera pose
Z. Yin and J. Shi · 2018
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Unsupervised learning of monocular depth estimation and visual odometry with deep feature reconstruction
H. Zhan, R. Garg, C. S. Weerasekera, K. Li, H. Agarwal, and I. Reid · 2018
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Df-net: Unsupervised joint learning of depth and flow using cross-task consistency
Y. Zou, Z. Luo, and J.-B. Huang · 2018
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