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This paper addresses the problem of depth estimation from a single still image.
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D. Hoiem, A. A. Efros, and M. Hebert · 2005
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Learning depth from single monocular images
A. Saxena, S. H. Chung, and A. Y. Ng · 2005
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A dynamic bayesian network model for autonomous 3d reconstruction from a single indoor image
E. Delage, H. Lee, and A. Y. Ng · 2006
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3-d depth reconstruction from a single still image
A. Saxena, S. H. Chung, and A. Y. Ng · 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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Fast high-dimensional filtering using the permutohedral lattice
A. Adams, J. Baek, and M. A. Davis · 2010
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Efficient inference in fully connected crfs with gaussian edge potentials
V. Koltun · 2011
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Multiscale convolutional neural networks for vision–based classification of cells
P. Buyssens, A. Elmoataz, and O. Lézoray · 2012
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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Indoor segmentation and support inference from rgbd images
N. Silberman, D. Hoiem, P. Kohli, and R. Fergus · 2012
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Continuous conditional random fields for efficient regression in large fully connected graphs
K. Ristovski, V. Radosavljevic, S. Vucetic, and Z. Obradovic · 2013
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A novel hand posture recognition system based on sparse representation using color and depth images
D. Xu, Y.-L. Chen, X. Wu, W. Feng, H. Qian, and Y. Xu · 2013
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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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Caffe: Convolutional architecture for fast feature embedding
Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell · 2014
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Depth transfer: Depth extraction from video using non-parametric sampling
K. Karsch, C. Liu, and S. B. Kang · 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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Deep convolutional neural fields for depth estimation from a single image
F. Liu, C. Shen, and G. Lin · 2015
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Fully convolutional networks for semantic segmentation
J. Long, E. Shelhamer, and T. Darrell · 2015
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Learning deconvolution network for semantic segmentation
H. Noh, S. Hong, and B. Han · 2015
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Towards unified depth and semantic prediction from a single image
P. Wang, X. Shen, Z. Lin, S. Cohen, B. Price, and A. Yuille · 2015
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Holistically-nested edge detection
S. Xie and Z. Tu · 2015
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Conditional random fields as recurrent neural networks
S. Zheng, S. Jayasumana, B. Romera-Paredes, V. Vineet, Z. Su, D. Du, C. Huang, and P. H. Torr · 2015
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V. Badrinarayanan, A. Handa, and R. Cipolla · 2015
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Deepedge: A multi-scale bifurcated deep network for top-down contour detection
G. Bertasius, J. Shi, and L. Torresani · 2015
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Semantic image segmentation with deep convolutional nets and fully connected crfs
L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille · 2015
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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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Hypercolumns for object segmentation and fine-grained localization
B. Hariharan, P. Arbeláez, R. Girshick, and J. Malik · 2015
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 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
Cited alongside, same era.
Indoor scene structure analysis for single image depth estimation
W. Zhuo, M. Salzmann, X. He, and M. Liu · 2015
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Attention to scale: Scale-aware semantic image segmentation
L.-C. Chen, Y. Yang, J. Wang, W. Xu, and A. L. Yuille · 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
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Monocular depth estimation using neural regression forest
A. Roy and S. Todorovic · 2016
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Learning depth-aware deep representations for robotic perception
L. Porzi, S. R. Buló, A. Penate-Sanchez, E. Ricci, and F. Moreno-Noguer · 2017
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Learning cross-modal deep representations for robust pedestrian detection
D. Xu, W. Ouyang, E. Ricci, X. Wang, and N. Sebe · 2017
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