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Convolutional neural networks are designed for dense data, but vision data is often sparse (stereo depth, point clouds, pen stroke, etc.).
Indoor segmentation and support inference from rgbd images
N. Silberman, D. Hoiem, P. Kohli, and R. Fergus · 2012
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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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Learning rich features from rgb-d images for object detection and segmentation
S. Gupta, R. Girshick, P. Arbeláez, and J. Malik · 2014
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Fully convolutional networks for semantic segmentation
J. Long, E. Shelhamer, and T. Darrell · 2015
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U-net: Convolutional networks for biomedical image segmentation
O. Ronneberger, P. Fischer, and T. Brox · 2015
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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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Deeper depth prediction with fully convolutional residual networks
I. Laina, C. Rupprecht, V. Belagiannis, F. Tombari, and N. Navab · 2016
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Understanding the effective receptive field in deep convolutional neural networks
W. Luo, Y. Li, R. Urtasun, and R. Zemel · 2016
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The synthia dataset: A large collection of synthetic images for semantic segmentation of urban scenes
G. Ros, L. Sellart, J. Materzynska, D. Vazquez, and A. M. Lopez · 2016
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Segnet: A deep convolutional encoder-decoder architecture for image segmentation
V. Badrinarayanan, A. Kendall, and R. Cipolla · 2017
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Multi-view 3d object detection network for autonomous driving
X. Chen, H. Ma, J. Wan, B. Li, and T. Xia · 2017
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Improved regularization of convolutional neural networks with cutout
T. DeVries and G. W. Taylor · 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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Submanifold sparse convolutional networks
B. Graham and L. van der Maaten · 2017
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Adapnet: Adaptive semantic segmentation in adverse environmental conditions
A. Valada, J. Vertens, A. Dhall, and W. Burgard · 2017
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Pyramid scene parsing network
H. Zhao, J. Shi, X. Qi, X. Wang, and J. Jia · 2017
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Learning transferable architectures for scalable image recognition
B. Zoph, V. Vasudevan, J. Shlens, and Q. V. Le · 2017
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Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs
L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille · 2018
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In defense of classical image processing: Fast depth completion on the cpu
J. Ku, A. Harakeh, and S. L. Waslander · 2018
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Y. Kuznietsov, J. Stückler, and B. Leibe · 2017
Cited alongside, same era.
Pointnet: Deep learning on point sets for 3d classification and segmentation
C. R. Qi, H. Su, K. Mo, and L. J. Guibas · 2017
Cited alongside, same era.
Octnet: Learning deep 3d representations at high resolutions
G. Riegler, A. O. Ulusoy, and A. Geiger · 2017
Cited alongside, same era.
Sparsity invariant cnns
J. Uhrig, N. Schneider, L. Schneidre, U. Franke, T. Brox, and A. Geiger · 2017
Cited alongside, same era.
Sparse-to-dense: Depth prediction from sparse depth samples and a single image
F. Ma and S. Karaman · 2018
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Sbnet: Sparse blocks network for fast inference
M. Ren, A. Pokrovsky, B. Yang, and R. Urtasun · 2018
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Deep depth completion of a single rgb-d image
Y. Zhang and T. Funkhouser · 2018
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