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Dense depth perception is critical for autonomous driving and other robotics applications.
J. M. Prewitt, “Object enhancement and extraction,” Picture processing and Psychopictorics , vol. 10, no. 1, pp. 15–19, 1970
1970
Earlier work this paper cites.
C. Tomasi and R. Manduchi, “Bilateral filtering for gray and color images.” in IEEE International Conference on Computer Vision (ICCV) , vol. 98, no. 1, 1998, p. 2
1998
Earlier work this paper cites.
A. Levin, D. Lischinski, and Y. Weiss, “Colorization using optimization,” in ACM transactions on graphics (TOG) , vol. 23, no. 3, 2004, pp. 689–694
2004
Earlier work this paper cites.
J. Kopf, M. F. Cohen, D. Lischinski, and M. Uyttendaele, “Joint bilateral upsampling,” in ACM Transactions on Graphics (ToG) , vol. 26, no. 3. ACM, 2007, p. 96
2007
Earlier work this paper cites.
Q. Yang, R. Yang, J. Davis, and D. Nistér, “Spatial-depth super resolution for range images,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2007, pp. 1–8
2007
Earlier work this paper cites.
H. Hirschmuller, “Stereo processing by semiglobal matching and mutual information,” IEEE Transactions on pattern analysis and machine intelligence (TPAMI) , vol. 30, no. 2, pp. 328–341, 2008
2008
Earlier work this paper cites.
A. Levin, D. Lischinski, and Y. Weiss, “A closed-form solution to natural image matting,” IEEE transactions on pattern analysis and machine intelligence (TPAMI) , vol. 30, no. 2, pp. 228–242, 2008
2008
Earlier work this paper cites.
H. Hirschmuller and D. Scharstein, “Evaluation of stereo matching costs on images with radiometric differences,” IEEE transactions on pattern analysis and machine intelligence (TPAMI) , vol. 31, no. 9, pp. 1582–1599, 2009
2009
Earlier work this paper cites.
K. He, J. Sun, and X. Tang, “Guided image filtering,” in European conference on computer vision (ECCV) , 2010, pp. 1–14
2010
Earlier work this paper cites.
S. Hawe, M. Kleinsteuber, and K. Diepold, “Dense disparity maps from sparse disparity measurements,” in IEEE International Conference on Computer Vision (ICCV) , 2011, pp. 2126–2133
2011
Earlier work this paper cites.
J. Park, H. Kim, Y.-W. Tai, M. S. Brown, and I. Kweon, “High quality depth map upsampling for 3d-tof cameras,” in International Conference on Computer Vision (ICCV) . IEEE, 2011, pp. 1623–1630
2011
Earlier work this paper cites.
O. Mac Aodha, N. D. Campbell, A. Nair, and G. J. Brostow, “Patch based synthesis for single depth image super-resolution,” in European conference on computer vision (ECCV) , 2012, pp. 71–84
2012
Earlier work this paper cites.
N. Silberman, D. Hoiem, P. Kohli, and R. Fergus, “Indoor segmentation and support inference from rgbd images,” in European Conference on Computer Vision (ECCV) , 2012, pp. 746–760
2012
Earlier work this paper cites.
K. He, J. Sun, and X. Tang, “Guided image filtering,” IEEE transactions on pattern analysis and machine intelligence (TPAMI) , vol. 35, no. 6, pp. 1397–1409, 2013
2013
Earlier work this paper cites.
M.-Y. Liu, O. Tuzel, and Y. Taguchi, “Joint geodesic upsampling of depth images,” in IEEE conference on computer vision and pattern recognition (CVPR) , 2013, pp. 169–176
2013
Earlier work this paper cites.
M. Hornacek, C. Rhemann, M. Gelautz, and C. Rother, “Depth super resolution by rigid body self-similarity in 3d,” in IEEE conference on computer vision and pattern recognition (CVPR) , 2013, pp. 1123–1130
2013
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 IEEE International Conference on Computer Vision (ICCV) , 2013, pp. 993–1000
2013
Earlier work this paper cites.
Q. Yan, X. Shen, L. Xu, S. Zhuo, X. Zhang, L. Shen, and J. Jia, “Cross-field joint image restoration via scale map,” in IEEE International Conference on Computer Vision (ICCV) , 2013, pp. 1537–1544
2013
Earlier work this paper cites.
A. Janoch, S. Karayev, Y. Jia, J. T. Barron, M. Fritz, K. Saenko, and T. Darrell, “A category-level 3d object dataset: Putting the kinect to work,” in IEEE International Conference on Computer Vision Workshop (ICCVW) . Springer, 2013, pp. 141–165
2013
Earlier work this paper cites.
J. Xiao, A. Owens, and A. Torralba, “Sun3d: A database of big spaces reconstructed using sfm and object labels,” in IEEE International Conference on Computer Vision (ICCV) , 2013, pp. 1625–1632
2013
Earlier work this paper cites.
J. Park, H. Kim, Y.-W. Tai, M. S. Brown, and I. S. Kweon, “High-quality depth map upsampling and completion for rgb-d cameras,” IEEE Transactions on Image Processing (TIP) , vol. 23, no. 12, pp. 5559–5572, 2014
2014
Earlier work this paper cites.
Q. Zhang, X. Shen, L. Xu, and J. Jia, “Rolling guidance filter,” in European conference on computer vision (ECCV) . Springer, 2014, pp. 815–830
2014
Cited alongside, same era.
2014
Cited alongside, same era.
J. Zbontar and Y. LeCun, “Computing the stereo matching cost with a convolutional neural network,” in IEEE conference on computer vision and pattern recognition (CVPR) , 2015, pp. 1592–1599
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 (TIP) , vol. 24, no. 6, pp. 1983–1996, 2015
2015
Cited alongside, same era.
J. Dai, H. Qi, Y. Xiong, Y. Li, G. Zhang, H. Hu, and Y. Wei, “Deformable convolutional networks,” in IEEE international conference on computer vision (ICCV) , 2017, pp. 764–773
2017
Later among the works it cites.
M. Simonovsky and N. Komodakis, “Dynamic edge-conditioned filters in convolutional neural networks on graphs,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2017, pp. 3693–3702
2017
Later among the works it cites.
2018
Later among the works it cites.
2018
Later among the works it cites.
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D. Ferstl, M. Ruther, and H. Bischof, “Variational depth superresolution using example-based edge representations,” in IEEE International Conference on Computer Vision (ICCV) , 2015, pp. 513–521
2015
Cited alongside, same era.
J. Xie, R. S. Feris, S.-S. Yu, and M.-T. Sun, “Joint super resolution and denoising from a single depth image,” IEEE Transactions on Multimedia , vol. 17, no. 9, pp. 1525–1537, 2015
2015
Cited alongside, same era.
X. Shen, C. Zhou, L. Xu, and J. Jia, “Mutual-structure for joint filtering,” in IEEE International Conference on Computer Vision (ICCV) , 2015, pp. 3406–3414
2015
Cited alongside, same era.
B. Ham, M. Cho, and J. Ponce, “Robust image filtering using joint static and dynamic guidance,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2015, pp. 4823–4831
2015
Cited alongside, same era.
S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” in International Conference on Machine Learning (ICLR) , 2015, pp. 448–456
2015
Cited alongside, same era.
S. Song, S. P. Lichtenberg, and J. Xiao, “Sun rgb-d: A rgb-d scene understanding benchmark suite,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2015, pp. 567–576
2015
Cited alongside, same era.
W. Luo, A. G. Schwing, and R. Urtasun, “Efficient deep learning for stereo matching,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2016, pp. 5695–5703
2016
Cited alongside, same era.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2016, pp. 770–778
2016
Cited alongside, same era.
J. Ku, A. Harakeh, and S. L. Waslander, “In defense of classical image processing: Fast depth completion on the cpu,” in 15th Conference on Computer and Robot Vision (CRV) , 2018, pp. 16–22
2018
Later among the works it cites.
M. Jaritz, R. De Charette, E. Wirbel, X. Perrotton, and F. Nashashibi, “Sparse and dense data with cnns: Depth completion and semantic segmentation,” in International Conference on 3D Vision (3DV) , 2018, pp. 52–60
2018
Later among the works it cites.
A. Eldesokey, M. Felsberg, and F. S. Khan, “Propagating confidences through cnns for sparse data regression,” British Machine Vision Conference (BMVC) , 2018
2018
Later among the works it cites.
N. Chodosh, C. Wang, and S. Lucey, “Deep convolutional compressed sensing for lidar depth completion,” 2018
2018
Later among the works it cites.
Y. Zhang and T. Funkhouser, “Deep depth completion of a single rgb-d image,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2018, pp. 175–185
2018
Later among the works it cites.
2018
Later among the works it cites.
2018
Later among the works it cites.
X. Cheng, P. Wang, and R. Yang, “Depth estimation via affinity learned with convolutional spatial propagation network,” in European Conference on Computer Vision (ECCV) , 2018, pp. 103–119
2018
Later among the works it cites.
H. Wu, S. Zheng, J. Zhang, and K. Huang, “Fast end-to-end trainable guided filter,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2018, pp. 1838–1847
2018
Later among the works it cites.
J. Wu, D. Li, Y. Yang, C. Bajaj, and X. Ji, “Dynamic filtering with large sampling field for convnets,” in European Conference on Computer Vision (ECCV) , 2018, pp. 185–200
2018
Later among the works it cites.
H. Zhang, K. Dana, J. Shi, Z. Zhang, X. Wang, A. Tyagi, and A. Agrawal, “Context encoding for semantic segmentation,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , June 2018
2018
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 IEEE International Conference on Robotics and Automation (ICRA) , 2018, pp. 1–8
2018
Later among the works it cites.
2018
Later among the works it cites.
2019
Closest in time.
B.-U. Lee, H.-G. Jeon, S. Im, and I. S. Kweon, “Depth completion with deep geometry and context guidance,” in IEEE International Conference on Robotics and Automation (ICRA) , 2019
2019
Closest in time.