Plug-and-Play: Improve Depth Estimation via Sparse Data Propagation

D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning internal representations by error propagation,” in Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Volume 1: Foundations , 1986

1986

Earlier work this paper cites.

H. Fu, M. Gong, C. Wang, K. Batmanghelich, and D. Tao, “Deep ordinal regression network for monocular depth estimation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 2002–2011

2011

Earlier work this paper cites.

S. Hawe, M. Kleinsteuber, and K. Diepold, “Dense disparity maps from sparse disparity measurements,” in 13th International Conference on Computer Vision , 2011

2011

Earlier work this paper cites.

P. K. Nathan Silberman, Derek Hoiem and R. Fergus, “Indoor segmentation and support inference from rgbd images,” in European Conference on Computer Vision (ECCV) , 2012

2012

Earlier work this paper cites.

A. Geiger, P. Lenz, and R. Urtasun, “Are we ready for autonomous driving? the kitti vision benchmark suite,” in Conference on Computer Vision and Pattern Recognition (CVPR) , 2012

2012

Earlier work this paper cites.

D. Ferstl, C. Reinbacher, R. Ranftl, M. Rüther, and H. Bischof, “Image guided depth upsampling using anisotropic total generalized variation,” in Proceedings of the IEEE International Conference on Computer Vision , 2013, pp. 993–1000

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 Advances in neural information processing systems , 2014, pp. 2366–2374

2014

Earlier work this paper cites.

D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in International Conference for Learning Representations , 2014

2014

Earlier work this paper cites.

J. Yosinski, J. Clune, A. Nguyen, T. Fuchs, and H. Lipson, “Understanding neural networks through deep visualization,” in Deep Learning Workshop, International Conference on Machine Learning (ICML) , 2015

2015

Earlier work this paper cites.

D. Eigen and R. Fergus, “Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture,” in Proceedings of the IEEE International Conference on Computer Vision , 2015, pp. 2650–2658

2015

Cited alongside, same era.

F. Liu, C. Shen, and G. Lin, “Deep convolutional neural fields for depth estimation from a single image,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2015, pp. 5162–5170

2015

Cited alongside, same era.

L.-K. Liu, S. H. Chan, and T. Q. Nguyen, “Depth reconstruction from sparse samples: Representation, algorithm, and sampling,” IEEE Transactions on Image Processing , vol. 24, no. 6, pp. 1983–1996, 2015

2015

Cited alongside, same era.

A. Kurakin, I. Goodfellow, and S. Bengio, “Adversarial examples in the physical world,” arXiv preprint arXiv:1607.02533 , 2016

Original

2016

Cited alongside, same era.

Y. Cao, Z. Wu, and C. Shen, “Estimating depth from monocular images as classification using deep fully convolutional residual networks,” IEEE Transactions on Circuits and Systems for Video Technology , 2017

2017

Later among the works it cites.

T. Zhou, M. Brown, N. Snavely, and D. G. Lowe, “Unsupervised learning of depth and ego-motion from video,” in CVPR , 2017

2017

Later among the works it cites.

F. Ma and S. Karaman, “Sparse-to-dense: Depth prediction from sparse depth samples and a single image,” in International Conference on Robotics and Automation (ICRA) , 2018

2018

Closest in time.

M. Jaritz, R. De Charette, E. Wirbel, X. Perrotton, and F. Nashashibi, “Sparse and dense data with cnns: Depth completion and semantic segmentation,” arXiv preprint arXiv:1808.00769 , 2018

Original

2018

Closest in time.

Z. Chen, V. Badrinarayanan, G. Drozdov, and A. Rabinovich, “Estimating depth from rgb and sparse sensing,” arXiv preprint arXiv:1804.02771 , 2018