Fetching the paper…
Reading the bibliography…
Estimating accurate depth from a single image is challenging because it is an ill-posed problem as infinitely many 3D scenes can be projected to the same 2D scene.
A taxonomy and evaluation of dense two-frame stereo correspondence algorithms
D. Scharstein and R. Szeliski · 2002
Earlier work this paper cites.
Learning depth from single monocular images
A. Saxena, S. H. Chung, and A. Y. Ng · 2006
Earlier work this paper cites.
Photometric stereo with general, unknown lighting
R. Basri, D. Jacobs, and I. Kemelmacher · 2007
Earlier work this paper cites.
Make3d: Learning 3d scene structure from a single still image
A. Saxena, M. Sun, and A. Y. Ng · 2009
Earlier work this paper cites.
Heliometric stereo: Shape from sun position
A. Abrams, C. Hawley, and R. Pless · 2012
Earlier work this paper cites.
Perceiving in depth, volume 1: basic mechanisms
I. P. Howard · 2012
Earlier work this paper cites.
Indoor segmentation and support inference from rgbd images
N. Silberman, D. Hoiem, P. Kohli, and R. Fergus · 2012
Earlier work this paper cites.
Vision meets robotics: The kitti dataset
A. Geiger, P. Lenz, C. Stiller, and R. Urtasun · 2013
Earlier work this paper cites.
Depth map prediction from a single image using a multi-scale deep network
D. Eigen, C. Puhrsch, and R. Fergus · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
Earlier work this paper cites.
Fast and accurate deep network learning by exponential linear units (elus)
D.-A. Clevert, T. Unterthiner, and S. Hochreiter · 2015
Earlier work this paper cites.
Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture
D. Eigen and R. Fergus · 2015
Earlier work this paper cites.
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
Earlier work this paper cites.
Imagenet large scale visual recognition challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, et al · 2015
Earlier work this paper cites.
Fully connected deep structured networks
A. G. Schwing and R. Urtasun · 2015
Earlier work this paper cites.
Towards unified depth and semantic prediction from a single image
P. Wang, X. Shen, Z. Lin, S. Cohen, B. Price, and A. L. Yuille · 2015
Earlier work this paper cites.
Designing deep networks for surface normal estimation
X. Wang, D. Fouhey, and A. Gupta · 2015
Earlier work this paper cites.
Depth from a single image by harmonizing overcomplete local network predictions
A. Chakrabarti, J. Shao, and G. Shakhnarovich · 2016
Cited alongside, same era.
Single-image depth perception in the wild
W. Chen, Z. Fu, D. Yang, and J. Deng · 2016
Cited alongside, same era.
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
Cited alongside, same era.
Unsupervised cnn for single view depth estimation: Geometry to the rescue
R. Garg, V. K. BG, G. Carneiro, and I. Reid · 2016
Cited alongside, same era.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Cited alongside, same era.
Unified depth prediction and intrinsic image decomposition from a single image via joint convolutional neural fields
S. Kim, K. Park, K. Sohn, and S. Lin · 2016
A two-streamed network for estimating fine-scaled depth maps from single rgb images
J. Li, R. Klein, and A. Yao · 2017
Later among the works it cites.
Aggregated residual transformations for deep neural networks
S. Xie, R. Girshick, P. Dollar, Z. Tu, and K. He · 2017
Later among the works it cites.
Multi-scale continuous crfs as sequential deep networks for monocular depth estimation
D. Xu, E. Ricci, W. Ouyang, X. Wang, and N. Sebe · 2017
Later among the works it cites.
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
Later among the works it cites.
Deep ordinal regression network for monocular depth estimation
H. Fu, M. Gong, C. Wang, K. Batmanghelich, and D. Tao · 2018
Later among the works it cites.
Monocular depth estimation with affinity, vertical pooling, and label enhancement
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Deeper depth prediction with fully convolutional residual networks
I. Laina, C. Rupprecht, V. Belagiannis, F. Tombari, and N. Navab · 2016
Cited alongside, same era.
Learning depth from single monocular images using deep convolutional neural fields
F. Liu, C. Shen, G. Lin, and I. D. Reid · 2016
Cited alongside, same era.
Deconvolution and checkerboard artifacts
A. Odena, V. Dumoulin, and C. Olah · 2016
Cited alongside, same era.
Dense monocular depth estimation in complex dynamic scenes
R. Ranftl, V. Vineet, Q. Chen, and V. Koltun · 2016
Cited alongside, same era.
Monocular depth estimation using neural regression forest
A. Roy and S. Todorovic · 2016
Cited alongside, same era.
Deep3d: Fully automatic 2d-to-3d video conversion with deep convolutional neural networks
J. Xie, R. Girshick, and A. Farhadi · 2016
Cited alongside, same era.
Y. Gan, X. Xu, W. Sun, and L. Lin · 2018
Later among the works it cites.
Single-image depth estimation based on fourier domain analysis
J.-H. Lee, M. Heo, K.-R. Kim, and C.-S. Kim · 2018
Later among the works it cites.
Unsupervised learning of depth and ego-motion from monocular video using 3d geometric constraints
R. Mahjourian, M. Wicke, and A. Angelova · 2018
Later among the works it cites.
Geonet: Geometric neural network for joint depth and surface normal estimation
X. Qi, R. Liao, Z. Liu, R. Urtasun, and J. Jia · 2018
Later among the works it cites.
Mobilenetv2: Inverted residuals and linear bottlenecks
M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen · 2018
Later among the works it cites.
Learning depth from monocular videos using direct methods
C. Wang, J. Miguel Buenaposada, R. Zhu, and S. Lucey · 2018
Later among the works it cites.
Denseaspp for semantic segmentation in street scenes
M. Yang, K. Yu, C. Zhang, Z. Li, and K. Yang · 2018
Later among the works it cites.
Geonet: Unsupervised learning of dense depth, optical flow and camera pose
Z. Yin and J. Shi · 2018
Later among the works it cites.
Soft labels for ordinal regression
R. Diaz and A. Marathe · 2019
Closest in time.
Digging into self-supervised monocular depth prediction
C. Godard, O. Mac Aodha, M. Firman, and G. J. Brostow · 2019
Closest in time.
Pytorch: An imperative style, high-performance deep learning library
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Kopf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala · 2019
Closest in time.
Enforcing geometric constraints of virtual normal for depth prediction
W. Yin, Y. Liu, C. Shen, and Y. Yan · 2019
Closest in time.