Fetching the paper…
Reading the bibliography…
Depth estimation from single monocular images is a key component of scene understanding and has benefited largely from deep convolutional neural networks (CNN) recently.
A. Saxena, A. Ng, and S. Chung, “Learning Depth from Single Monocular Images,” Proc. Adv. Neural Inf. Process. Syst. , 2005
2005
Earlier work this paper cites.
A. Saxena, S. H. Chung, and A. Y. Ng, “3-d depth reconstruction from a single still image,” Int. J. Comp. Vis. , 2007
2007
Earlier work this paper cites.
B. C. Russell and A. Torralba, “Building a database of 3d scenes from user annotations.” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn. , 2009
2009
Earlier work this paper cites.
A. Saxena, M. Sun, and A. Y. Ng, “Make3D: Learning 3d scene structure from a single still image,” IEEE Trans. Pattern Anal. Mach. Intell. , 2009
2009
Earlier work this paper cites.
V. Hedau, D. Hoiem, and D. Forsyth, “Thinking inside the box: Using appearance models and context based on room geometry,” in Proc. Eur. Conf. Comp. Vis. , 2010, pp. 224–237
2010
Earlier work this paper cites.
A. Gupta, M. Hebert, T. Kanade, and D. M. Blei, “Estimating spatial layout of rooms using volumetric reasoning about objects and surfaces,” in Proc. Adv. Neural Inf. Process. Syst. , 2010
2010
Earlier work this paper cites.
B. Liu, S. Gould, and D. Koller, “Single image depth estimation from predicted semantic labels,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn. , 2010
2010
Earlier work this paper cites.
P. Krähenbühl and V. Koltun, “Efficient inference in fully connected CRFs with gaussian edge potentials,” in Proc. Adv. Neural Inf. Process. Syst. , 2011
2011
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Proc. Adv. Neural Inf. Process. Syst. , 2012, pp. 1097–1105
2012
Earlier work this paper cites.
A. G. Schwing and R. Urtasun, “Efficient exact inference for 3d indoor scene understanding,” in Proc. Eur. Conf. Comp. Vis. , 2012
2012
Earlier work this paper cites.
N. Silberman, D. Hoiem, P. Kohli, and R. Fergus, “Indoor segmentation and support inference from rgbd images,” in Proc. Eur. Conf. Comp. Vis. , 2012
2012
Earlier work this paper cites.
A. Geiger, P. Lenz, C. Stiller, and R. Urtasun, “Vision meets robotics: The kitti dataset,” Int. J. Robt. Res. , 2013
2013
Earlier work this paper cites.
D. Eigen, C. Puhrsch, and R. Fergus, “Depth map prediction from a single image using a multi-scale deep network,” in Proc. Adv. Neural Inf. Process. Syst. , 2014
2014
Cited alongside, same era.
K. Karsch, C. Liu, and S. B. Kang, “Depthtransfer: Depth extraction from video using non-parametric sampling,” IEEE Trans. Pattern Anal. Mach. Intell. , 2014
2014
Cited alongside, same era.
L. Ladicky, J. Shi, and M. Pollefeys, “Pulling things out of perspective,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn. , 2014
2014
Cited alongside, same era.
M. Liu, M. Salzmann, and X. He, “Discrete-continuous depth estimation from a single image,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn. , 2014
2014
Cited alongside, same era.
D. Eigen and R. Fergus, “Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture,” in Proc. IEEE Int. Conf. Comp. Vis. , 2015
2015
Later among the works it cites.
F. Liu, C. Shen, G. Lin, and I. D. Reid, “Learning depth from single monocular images using deep convolutional neural fields,” IEEE Trans. Pattern Anal. Mach. Intell. , 2016
2016
Closest in time.
2016
Closest in time.
Y. Gal and Z. Ghahramani, “Bayesian convolutional neural networks with Bernoulli approximate variational inference,” 2016
2016
Closest in time.
G. Lin, C. Shen, I. D. Reid, and A. van den Hengel, “Efficient piecewise training of deep structured models for semantic segmentation,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn. , 2016
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2015
Cited alongside, same era.
B. Li, C. Shen, Y. Dai, A. van den Hengel, and M. He, “Depth and surface normal estimation from monocular images using regression on deep features and hierarchical CRFs,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn. , 2015
2015
Cited alongside, same era.
P. Wang, X. Shen, Z. Lin, S. Cohen, B. Price, and A. L. Yuille, “Towards unified depth and semantic prediction from a single image,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn. , June 2015
2015
Cited alongside, same era.
2015
Cited alongside, same era.
L. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, “Semantic image segmentation with deep convolutional nets and fully connected CRFs,” Proc. IEEE Int. Conf. Learn. Rep. , 2015
2015
Cited alongside, same era.
K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” Proc. Int. Conf. Learn. Rep. , 2015
2015
Cited alongside, same era.
S. Song, S. P. Lichtenberg, and J. Xiao, “SUN RGB-D: A rgb-d scene understanding benchmark suite,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn. , 2015
2015
Cited alongside, same era.
B. Hariharan, P. Arbeláez, R. Girshick, and J. Malik, “Hypercolumns for object segmentation and fine-grained localization,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn. , 2015
2015
Cited alongside, same era.
2016
Closest in time.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn. , 2016
2016
Closest in time.
A. Roy and S. Todorovic, “Monocular depth estimation using neural regression forest,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn. , 2016
2016
Closest in time.
I. Laina, C. Rupprecht, V. Belagiannis, F. Tombari, and N. Navab, “Deeper depth prediction with fully convolutional residual networks,” in Proc. IEEE Int. Conf. 3D Vision , October 2016
2016
Closest in time.
2016
Closest in time.
M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele, “The cityscapes dataset for semantic urban scene understanding,” in Proc. IEEE Conf. Comp. Vis. Patt. Recogn. , 2016
2016
Closest in time.
R. Garg and I. Reid, “Unsupervised cnn for single view depth estimation: Geometry to the rescue,” in Proc. Eur. Conf. Comp. Vis. , 2016
2016
Closest in time.