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Depth completion aims to recover a dense depth map from the sparse depth data and the corresponding single RGB image.
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2011
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2012
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2012
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2013
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2014
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K. Simonyan and A. Zisserman, “Two-stream convolutional networks for action recognition in videos,” in
2014
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2014
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D. Eigen and R. Fergus, “Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture,” in
2015
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N. Schneider, L. Schneider, P. Pinggera, U. Franke, M. Pollefeys, and C. Stiller, “Semantically guided depth upsampling,” in
2016
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2016
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2016
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R. Garg, V. K. BG, G. Carneiro, and I. Reid, “Unsupervised cnn for single view depth estimation: Geometry to the rescue,” in
2016
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2016
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F. Liu, C. Shen, G. Lin, and I. Reid, “Learning depth from single monocular images using deep convolutional neural fields,”
2016
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K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in
2016
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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
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J. Uhrig, N. Schneider, L. Schneider, U. Franke, T. Brox, and A. Geiger, “Sparsity invariant cnns,” in
2017
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2017
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C. Godard, O. Mac Aodha, and G. J. Brostow, “Unsupervised monocular depth estimation with left-right consistency,” in
2017
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T. Zhou, M. Brown, N. Snavely, and D. G. Lowe, “Unsupervised learning of depth and ego-motion from video,” in
2017
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V. Garcia and J. Bruna, “Few-shot learning with graph neural networks,”
2017
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2017
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A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” in
2017
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D. Xu, D. Anguelov, and A. Jain, “Pointfusion: Deep sensor fusion for 3d bounding box estimation,” in
2018
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G. Moon, J. Yong Chang, and K. Mu Lee, “V2v-posenet: Voxel-to-voxel prediction network for accurate 3d hand and human pose estimation from a single depth map,” in
2018
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Y. Zhang and T. Funkhouser, “Deep depth completion of a single rgb-d image,” in
2018
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Y. Chen, B. Yang, M. Liang, and R. Urtasun, “Learning joint 2d-3d representations for depth completion,” in
2019
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Y. Zhong, C.-Y. Wu, S. You, and U. Neumann, “Deep rgb-d canonical correlation analysis for sparse depth completion,” in
2019
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A. Eldesokey, M. Felsberg, and F. S. Khan, “Confidence propagation through cnns for guided sparse depth regression,”
2019
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J. Qiu, Z. Cui, Y. Zhang, X. Zhang, S. Liu, B. Zeng, and M. Pollefeys, “Deeplidar: Deep surface normal guided depth prediction for outdoor scene from sparse lidar data and single color image,” in
2019
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Y. Xu, X. Zhu, J. Shi, G. Zhang, H. Bao, and H. Li, “Depth completion from sparse lidar data with depth-normal constraints,” in
2019
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F. Ma and S. Karaman, “Sparse-to-dense: Depth prediction from sparse depth samples and a single image,” in
2018
Cited alongside, same era.
M. Jaritz, R. De Charette, E. Wirbel, X. Perrotton, and F. Nashashibi, “Sparse and dense data with cnns: Depth completion and semantic segmentation,” in
2018
Cited alongside, same era.
2018
Cited alongside, same era.
2018
Cited alongside, same era.
X. Cheng, P. Wang, and R. Yang, “Depth estimation via affinity learned with convolutional spatial propagation network,” in
2018
Cited alongside, same era.
Y. Yang and S. Soatto, “Conditional prior networks for optical flow,” in
2018
Cited alongside, same era.
2018
Cited alongside, same era.
2019
Later among the works it cites.
W. Van Gansbeke, D. Neven, B. De Brabandere, and L. Van Gool, “Sparse and noisy lidar completion with rgb guidance and uncertainty,” in
2019
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F. Ma, G. V. Cavalheiro, and S. Karaman, “Self-supervised sparse-to-dense: Self-supervised depth completion from lidar and monocular camera,” in
2019
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A. Atapour-Abarghouei and T. P. Breckon, “To complete or to estimate, that is the question: A multi-task approach to depth completion and monocular depth estimation,” in
2019
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Y. Yang, A. Wong, and S. Soatto, “Dense depth posterior (ddp) from single image and sparse range,” in
2019
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S. Zhao, H. Fu, M. Gong, and D. Tao, “Geometry-aware symmetric domain adaptation for monocular depth estimation,” in
2019
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L. Shi, Y. Zhang, J. Cheng, and H. Lu, “Skeleton-based action recognition with directed graph neural networks,” in
2019
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L. Wang, Y. Huang, Y. Hou, S. Zhang, and J. Shan, “Graph attention convolution for point cloud semantic segmentation,” in
2019
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J. Kim, T. Kim, S. Kim, and C. D. Yoo, “Edge-labeling graph neural network for few-shot learning,” in
2019
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W. Wu, Z. Qi, and L. Fuxin, “Pointconv: Deep convolutional networks on 3d point clouds,” in
2019
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A. Eldesokey, M. Felsberg, K. Holmquist, and M. Persson, “Uncertainty-aware cnns for depth completion: Uncertainty from beginning to end,” in
2020
Closest in time.
K. Lu, N. Barnes, S. Anwar, and L. Zheng, “From depth what can you see? depth completion via auxiliary image reconstruction,” in
2020
Closest in time.
Z. Huang, J. Fan, S. Cheng, S. Yi, X. Wang, and H. Li, “Hms-net: Hierarchical multi-scale sparsity-invariant network for sparse depth completion,”
2020
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X. Cheng, P. Wang, C. Guan, and R. Yang, “Cspn++: Learning context and resource aware convolutional spatial propagation networks for depth completion.” in
2020
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A. Li, Z. Yuan, Y. Ling, W. Chi, C. Zhang
2020
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2020
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X. Xiong, H. Xiong, K. Xian, C. Zhao, Z. Cao, and X. Li, “Sparse-to-dense depth completion revisited: Sampling strategy and graph construction,” in
2020
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A. Wang, Z. Fang, Y. Gao, S. Tan, S. Wang, S. Ma, and J. Hwang, “Adversarial learning for joint optimization of depth and ego-motion,”
2020
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H. Yang, P. Chen, K. Chen, C. Lee, and Y. Chen, “Fade: Feature aggregation for depth estimation with multi-view stereo,”
2020
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K. PNVR, H. Zhou, and D. Jacobs, “Sharingan: Combining synthetic and real data for unsupervised geometry estimation,” in
2020
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Y. Wu, O. E. F. Bourahla, X. Li, F. Wu, Q. Tian, and X. Zhou, “Adaptive graph representation learning for video person re-identification,”
2020
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