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We present a novel approach for unsupervised learning of depth and ego-motion from monocular video.
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TensorFlow: Large-scale machine learning on heterogeneous systems
M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mané, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viégas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng · 2015
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Spatial transformer networks
M. Jaderberg, K. Simonyan, A. Zisserman, et al · 2015
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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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Learning depth from single monocular images using deep convolutional neural fields
F. Liu, C. Shen, G. Lin, and I. Reid · 2015
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Deeper depth prediction with fully convolutional residual networks
I. Laina, C. Rupprecht, V. Belagiannis, F. Tombari, and N. Navab · 2016
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A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation
N. Mayer, E. Ilg, P. Hausser, P. Fischer, D. Cremers, A. Dosovitskiy, and T. Brox · 2016
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Dense monocular depth estimation in complex dynamic scenes
R. Ranftl, V. Vineet, Q. Chen, and V. Koltun · 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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Stereo matching by training a convolutional neural network to compare image patches
J. Zbontar and Y. LeCun · 2016
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Fully convolutional networks for semantic segmentation
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Estimating depth from monocular images as classification using deep fully convolutional residual networks
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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
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Unsupervised cnn for single view depth estimation: Geometry to the rescue
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U-net: Convolutional networks for biomedical image segmentation
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Demon: Depth and motion network for learning monocular stereo
B. Ummenhofer, H. Zhou, J. Uhrig, N. Mayer, E. Ilg, A. Dosovitskiy, and T. Brox
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Unsupervised monocular depth estimation with left-right consistency
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Learning a multi-view stereo machine
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Sfm-net: Learning of structure and motion from video
S. Vijayanarasimhan, S. Ricco, C. Schmid, R. Sukthankar, and K. Fragkiadaki · 2017
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Loss functions for neural networks for image processing
H. Zhao, O. Gallo, I. Frosio, and J. Kautz · 2017
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Unsupervised learning of depth and ego-motion from video
T. Zhou, M. Brown, N. Snavely, and D. Lowe · 2017
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