Fetching the paper…
Reading the bibliography…
Depth completion, the technique of estimating a dense depth image from sparse depth measurements, has a variety of applications in robotics and autonomous driving.
Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography
M. A. Fischler and R. C. Bolles · 1987
Earlier work this paper cites.
An application of markov random fields to range sensing
J. Diebel and S. Thrun · 2006
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.
Epnp: An accurate o (n) solution to the pnp problem
V. Lepetit, F. Moreno-Noguer, and P. Fua · 2009
Earlier work this paper cites.
Kinectfusion: Real-time dense surface mapping and tracking
R. A. Newcombe, S. Izadi, O. Hilliges, D. Molyneaux, D. Kim, A. J. Davison, P. Kohi, J. Shotton, S. Hodges, and A. Fitzgibbon · 2011
Earlier work this paper cites.
Efficient spatio-temporal hole filling strategy for kinect depth maps
M. Camplani and L. Salgado · 2012
Earlier work this paper cites.
Layer depth denoising and completion for structured-light rgb-d cameras
J. Shen and S.-C. S. Cheung · 2013
Earlier work this paper cites.
Depth super resolution by rigid body self-similarity in 3d
M. Hornácek, C. Rhemann, M. Gelautz, and C. Rother · 2013
Earlier work this paper cites.
Loam: Lidar odometry and mapping in real-time
J. Zhang and S. Singh · 2014
Earlier work this paper cites.
Depth enhancement via low-rank matrix completion
S. Lu, X. Ren, and F. Liu · 2014
Earlier work this paper cites.
Single depth image super resolution and denoising via coupled dictionary learning with local constraints and shock filtering
J. Xie, C.-C. Chou, R. Feris, and M.-T. Sun · 2014
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.
Fast lidar localization using multiresolution gaussian mixture maps
R. W. Wolcott and R. M. Eustice · 2015
Earlier work this paper cites.
Sparse depth super resolution
J. Lu and D. Forsyth · 2015
Cited alongside, same era.
U-net: Convolutional networks for biomedical image segmentation
O. Ronneberger, P. Fischer, and T. Brox · 2015
Cited alongside, same era.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
Cited alongside, same era.
Sparse sensing for resource-constrained depth reconstruction
F. Ma, L. Carlone, U. Ayaz, and S. Karaman · 2016
Cited alongside, same era.
The fast bilateral solver
J. T. Barron and B. Poole · 2016
Cited alongside, same era.
Edge-guided single depth image super resolution
J. Xie, R. S. Feris, and M.-T. Sun · 2016
Cited alongside, same era.
Sparse depth sensing for resource-constrained robots
F. Ma, L. Carlone, U. Ayaz, and S. Karaman · 2017
Later among the works it cites.
Sparse-to-dense: Depth prediction from sparse depth samples and a single image
F. Ma and S. Karaman · 2017
Later among the works it cites.
Unsupervised learning of depth and ego-motion from video
T. Zhou, M. Brown, N. Snavely, and D. G. Lowe · 2017
Later among the works it cites.
Undeepvo: Monocular visual odometry through unsupervised deep learning
R. Li, S. Wang, Z. Long, and D. Gu · 2017
Later among the works it cites.
Automatic differentiation in pytorch
A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer · 2017
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
N. Schneider, L. Schneider, P. Pinggera, U. Franke, M. Pollefeys, and C. Stiller · 2016
Cited alongside, same era.
Learning sparse high dimensional filters: Image filtering, dense crfs and bilateral neural networks
V. Jampani, M. Kiefel, and P. V. Gehler · 2016
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.
Demon: Depth and motion network for learning monocular stereo
B. Ummenhofer, H. Zhou, J. Uhrig, N. Mayer, E. Ilg, A. Dosovitskiy, and T. Brox · 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.
J. Uhrig, N. Schneider, L. Schneider, U. Franke, T. Brox, and A. Geiger · 2017
Cited alongside, same era.
Later among the works it cites.
Deep depth completion of a single rgb-d image
Y. Zhang and T. Funkhouser · 2018
Closest in time.
In defense of classical image processing: Fast depth completion on the cpu
J. Ku, A. Harakeh, and S. L. Waslander · 2018
Closest in time.
Propagating confidences through cnns for sparse data regression
A. Eldesokey, M. Felsberg, and F. S. Khan · 2018
Closest in time.
Deep convolutional compressed sensing for lidar depth completion
N. Chodosh, C. Wang, and S. Lucey · 2018
Closest in time.
Deep ordinal regression network for monocular depth estimation
H. Fu, M. Gong, C. Wang, K. Batmanghelich, and D. Tao · 2018
Closest in time.
Unsupervised learning of depth and ego-motion from monocular video using 3d geometric constraints
R. Mahjourian, M. Wicke, and A. Angelova · 2018
Closest in time.
Geonet: Unsupervised learning of dense depth, optical flow and camera pose
Z. Yin and J. Shi · 2018
Closest in time.