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Depth completion recovers a dense depth map from sensor measurements.
Diatom autofocusing in brightfield microscopy: A comparative study
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Dense disparity maps from sparse disparity measurements
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Indoor segmentation and support inference from rgbd images
Pushmeet Kohli Nathan Silberman, Derek Hoiem and Rob Fergus · 2012
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Depth map prediction from a single image using a multi-scale deep network
David Eigen, Christian Puhrsch, and Rob Fergus · 2014
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
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Depth reconstruction from sparse samples: Representation, algorithm, and sampling
L. Liu, S. H. Chan, and T. Q. Nguyen · 2015
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U-net: Convolutional networks for biomedical image segmentation
O. Ronneberger, P.Fischer, and T. Brox · 2015
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Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos, Kevin Murphy, and Alan L. Yuille · 2016
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Efficient deep learning for stereo matching
Wenjie Luo, Alexander G Schwing, and Raquel Urtasun · 2016
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Hengshuang Zhao, Jianping Shi, Xiaojuan Qi, Xiaogang Wang, and Jiaya Jia · 2016
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Matterport3d: Learning from rgb-d data in indoor environments
Angel Chang, Angela Dai, Thomas Funkhouser, Maciej Halber, Matthias Niessner, Manolis Savva, Shuran Song, Andy Zeng, and Yinda Zhang · 2017
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Rethinking atrous convolution for semantic image segmentation
Liang-Chieh Chen, George Papandreou, Florian Schroff, and Hartwig Adam · 2017
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Scannet: Richly-annotated 3d reconstructions of indoor scenes
Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, and Matthias Nießner · 2017
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Unsupervised monocular depth estimation with left-right consistency
C. Godard, O. M. Aodha, and G. J. Brostow · 2017
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Refinenet: Multi-path refinement networks for high-resolution semantic segmentation
Guosheng Lin, Anton Milan, Chunhua Shen, and Ian Reid · 2017
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Sparsity invariant cnns
Jonas Uhrig, Nick Schneider, Lukas Schneider, Uwe Franke, Thomas Brox, and Andreas Geiger · 2017
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Ambarella cvflow technology overview · 2018
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Vincent Casser, Soeren Pirk, Reza Mahjourian, and Anelia Angelova · 2018
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Depth estimation via affinity learned with convolutional spatial propagation network
Real-time joint semantic segmentation and depth estimation using asymmetric annotations
Vladimir Nekrasov, Thanuja Dharmasiri, Andrew Spek, Tom Drummond, Chunhua Shen, and Ian Reid · 2019
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Semantic image synthesis with spatially-adaptive normalization
T. Park, M. Liu, T. Wang, and J. Zhu · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
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Deeplidar: Deep surface normal guided depth prediction for outdoor scene from sparse lidar data and single color image
Jiaxiong Qiu, Zhaopeng Cui, Yinda Zhang, Xingdi Zhang, Shuaicheng Liu, Bing Zeng, and Marc Pollefeys · 2019
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Dfusenet: Deep fusion of rgb and sparse depth information for image guided dense depth completion
S. S. Shivakumar, T. Nguyen, I. D. Miller, S. W. Chen, V. Kumar, and C. J. Taylor · 2019
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Xinjing Cheng, Peng Wang, and Ruigang Yang · 2018
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Deep ordinal regression network for monocular depth estimation
Huan Fu, Mingming Gong, Chaohui Wang, Kayhan Batmanghelich, and Dacheng Tao · 2018
Cited alongside, same era.
Megadepth: Learning single-view depth prediction from internet photos
Zhengqi Li and Noah Snavely · 2018
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Learning for disparity estimation through feature constancy
Zhengfa Liang, Yiliu Feng, YGHLW Chen, and LQLZJ Zhang · 2018
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Sparse-to-dense: Depth prediction from sparse depth samples and a single image
Fangchang Ma and Sertac Karaman · 2018
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Light-weight refinenet for real-time semantic segmentation
Vladimir Nekrasov, Chunhua Shen, and Ian D. Reid · 2018
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Taskonomy: Disentangling task transfer learning
Amir R. Zamir, Alexander Sax, William B. Shen, Leonidas J. Guibas, Jitendra Malik, and Silvio Savarese · 2018
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EfficientNet: Rethinking model scaling for convolutional neural networks
Mingxing Tan and Quoc Le · 2019
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Learning guided convolutional network for depth completion
Jie Tang, Fei-Peng Tian, Wei Feng, Jian Li, and Ping Tan · 2019
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Sparse and noisy lidar completion with rgb guidance and uncertainty
W. Van Gansbeke, D. Neven, B. De Brabandere, and L. Van Gool · 2019
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Sparse and noisy lidar completion with rgb guidance and uncertainty
Wouter Van Gansbeke, Davy Neven, Bert De Brabandere, and Luc Van Gool · 2019
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FastDepth: Fast Monocular Depth Estimation on Embedded Systems
Wofk, Diana and Ma, Fangchang and Yang, Tien-Ju and Karaman, Sertac and Sze, Vivienne · 2019
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Depth completion from sparse lidar data with depth-normal constraints
Yan Xu, Xinge Zhu, Jianping Shi, Guofeng Zhang, Hujun Bao, and Hongsheng Li · 2019
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Dense depth posterior (ddp) from single image and sparse range
Yanchao Yang, Alex Wong, and Stefano Soatto · 2019
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Deep architecture with cross guidance between single image and sparse lidar data for depth completion
S. Lee, J. Lee, D. Kim, and J. Kim · 2020
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A multi-scale guided cascade hourglass network for depth completion
Ang Li, Zejian Yuan, Yonggen Ling, Wanchao Chi, Chong Zhang, et al · 2020
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A multi-scale guided cascade hourglass network for depth completion
Ang Li, Zejian Yuan, Yonggen Ling, Wanchao Chi, shenghao zhang, and Chong Zhang · 2020
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Suw-learn: Joint supervised, unsupervised, weakly supervised deep learning for monocular depth estimation
Haoyu Ren, Aman Raj, Mostafa El-Khamy, and Jungwon Lee · 2020
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Towards general purpose and geometry preserving single-view depth estimation, 2020
Mikhail Romanov, Nikolay Patatkin, Anna Vorontsova, and Anton Konushin · 2020
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