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As 3D movie viewing becomes mainstream and Virtual Reality (VR) market emerges, the demand for 3D contents is growing rapidly.
Depth from scattering
Cozman, F., Krotkov, E.: · 1997
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Depth-image-based rendering (dibr), compression, and transmission for a new approach on 3d-tv
Fehn, C.: · 2004
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Stereo processing by semiglobal matching and mutual information
Hirschmüller, H.: · 2008
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On the recovery of depth from a single defocused image
Zhuo, S., Sim, T.: · 2009
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Make3d: Learning 3d scene structure from a single still image
Saxena, A., Sun, M., Ng, A.Y.: · 2009
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3d-tv content creation: automatic 2d-to-3d video conversion
Zhang, L., Vázquez, C., Knorr, S.: · 2011
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Indoor segmentation and support inference from rgbd images
Nathan Silberman, Derek Hoiem, P.K., Fergus, R.: · 2012
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Learning-based, automatic 2d-to-3d image and video conversion
Konrad, J., Wang, M., Ishwar, P., Wu, C., Mukherjee, D.: · 2013
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Vision meets robotics: The kitti dataset
Geiger, A., Lenz, P., Stiller, C., Urtasun, R.: · 2013
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Theatrical market statistics
Motion Picture Association of America: · 2014
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Fully automatic 2d to 3d conversion with aid of high-level image features
Appia, V., Batur, U.: · 2014
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Im2depth: Scalable exemplar based depth transfer
Baig, M.H., Jagadeesh, V., Piramuthu, R., Bhardwaj, A., Di, W., Sundaresan, N.: · 2014
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Depth map prediction from a single image using a multi-scale deep network
Eigen, D., Puhrsch, C., Fergus, R.: · 2014
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Very deep convolutional networks for large-scale image recognition
Simonyan, K., Zisserman, A.: · 2014
End-to-end training of deep visuomotor policies
Levine, S., Finn, C., Darrell, T., Abbeel, P.: · 2015
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Deepstereo: Learning to predict new views from the world’s imagery
Flynn, J., Neulander, I., Philbin, J., Snavely, N.: · 2015
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Flownet: Learning optical flow with convolutional networks
Fischer, P., Dosovitskiy, A., Ilg, E., Häusser, P., Hazırbaş, C., Golkov, V., van der Smagt, P., Cremers, D., Brox, T.: · 2015
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Deep multi-scale video prediction beyond mean square error
Mathieu, M., Couprie, C., LeCun, Y.: · 2015
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Cited alongside, same era.
Deep convolutional neural fields for depth estimation from a single image
Liu, F., Shen, C., Lin, G.: · 2015
Cited alongside, same era.
Conditional random fields as recurrent neural networks
Zheng, S., Jayasumana, S., Romera-Paredes, B., Vineet, V., Su, Z., Du, D., Huang, C., Torr, P.H.: · 2015
Cited alongside, same era.
Wang, L., Xiong, Y., Wang, Z., Qiao, Y.: · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S., Szegedy, C.: · 2015
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Mxnet: A flexible and efficient machine learning library for heterogeneous distributed systems
Chen, T., Li, M., Li, Y., Lin, M., Wang, N., Wang, M., Xiao, T., Xu, B., Zhang, C., Zhang, Z.: · 2015
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