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Deep networks have recently enjoyed enormous success when applied to recognition and classification problems in computer vision, but their use in graphics problems has been limited.
A space-sweep approach to true multi-image matching
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The lumigraph
S. J. Gortler, R. Grzeszczuk, R. Szeliski, and M. F. Cohen · 1996
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Light field rendering
M. Levoy and P. Hanrahan · 1996
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View morphing
S. M. Seitz and C. R. Dyer · 1996
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Prediction error as a quality metric for motion and stereo
R. Szeliski · 1999
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Stereo matching with transparency and matting
R. Szeliski and P. Golland · 1999
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Image-based rendering using image-based priors
A. Fitzgibbon, Y. Wexler, and A. Zisserman · 2003
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C. L. Zitnick, S. B. Kang, M. Uyttendaele, S. Winder, and R. Szeliski · 2004
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Automatic photo pop-up
D. Hoiem, A. A. Efros, and M. Hebert · 2005
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Three-dimensional scene flow
S. Vedula, P. Rander, R. Collins, and T. Kanade · 2005
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Efficient dense stereo with occlusions for new view-synthesis by four-state dynamic programming
A. Criminisi, A. Blake, C. Rother, J. Shotton, and P. Torr · 2007
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On new view synthesis using multiview stereo
O. Woodford, I. Reid, P. Torr, and A. Fitzgibbon · 2007
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Floating textures
M. Eisemann, B. De Decker, M. Magnor, P. Bekaert, E. de Aguiar, N. Ahmed, C. Theobalt, and A. Sellent · 2008
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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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Adaptive subgradient methods for online learning and stochastic optimization
J. Duchi, E. Hazan, and Y. Singer · 2010
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Ambient point clouds for view interpolation
M. Goesele, J. Ackermann, S. Fuhrmann, C. Haubold, R. Klowsky, D. Steedly, and R. Szeliski · 2010
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Silhouette-aware warping for image-based rendering
G. Chaurasia, O. Sorkine, and G. Drettakis · 2011
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Stereopsis via deep learning
R. Memisevic and C. Conrad · 2011
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Large scale distributed deep networks
J. Dean, G. Corrado, R. Monga, K. Chen, M. Devin, M. Mao, M. Ranzato, A. Senior, P. Tucker, K. Yang, Q. V. Le, and A. Y. Ng · 2012
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Unsupervised learning of depth and motion
K. R. Konda and R. Memisevic · 2013
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The visual turing test for scene reconstruction
Q. Shan, R. Adams, B. Curless, Y. Furukawa, and S. M. Seitz · 2013
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Learning a deep convolutional network for image super-resolution
C. Dong, C. C. Loy, K. He, and X. Tang · 2014
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Depth map prediction from a single image using a multi-scale deep network
D. Eigen, C. Puhrsch, and R. Fergus · 2014
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DepthTransfer: Depth extraction from video using non-parametric sampling
K. Karsch, C. Liu, and S. B. Kang · 2014
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First-person hyper-lapse videos
J. Kopf, M. F. Cohen, and R. Szeliski · 2014
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2d-to-3d image conversion by learning depth from examples
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. Hinton · 2012
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Image denoising and inpainting with deep neural networks
J. Xie, L. Xu, and E. Chen · 2012
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Continuous markov random fields for robust stereo estimation
K. Yamaguchi, T. Hazan, D. A. McAllester, and R. Urtasun · 2012
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Depth synthesis and local warps for plausible image-based navigation
G. Chaurasia, S. Duchêne, O. Sorkine-Hornung, and G. Drettakis · 2013
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Street view motion-from-structure-from-motion
B. Klingner, D. Martin, and J. Roseborough · 2013
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On the number of linear regions of deep neural networks
G. Montufar, R. Pascanu, K. Cho, and Y. Bengio · 2014
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Designing deep networks for surface normal estimation
X. Wang, D. F. Fouhey, and A. Gupta · 2014
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Learning to generate chairs with convolutional neural networks
A. Dosovitskiy, J. Springenberg, and T. Brox · 2015
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Deep convolutional inverse graphics network
T. Kulkarni, W. Whitney, K. Pushmeet, and J. Tenenbaum · 2015
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Deep convolutional inverse graphics network
T. D. Kulkarni, W. Whitney, P. Kohli, and J. B. Tenenbaum · 2015
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Going deeper with convolutions
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2015
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