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Depth completion aims to derive a dense depth map from sparse depth measurements with a synchronized color image.
Bilateral filtering for gray and color images
Carlo Tomasi and Roberto Manduchi · 1998
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An application of markov random fields to range sensing
James Diebel and Sebastian Thrun · 2005
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Joint bilateral upsampling
Johannes Kopf, Michael F Cohen, Dani Lischinski, and Matt Uyttendaele · 2007
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Fusion of range and color images for denoising and resolution enhancement with a non-local filter
Benjamin Huhle, Timo Schairer, Philipp Jenke, and Wolfgang Straßer · 2010
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Are we ready for autonomous driving? the kitti vision benchmark suite
Andreas Geiger, Philip Lenz, and Raquel Urtasun · 2012
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Guided image filtering
Kaiming He, Jian Sun, and Xiaoou Tang · 2012
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Indoor segmentation and support inference from rgbd images
Nathan Silberman, Derek Hoiem, Pushmeet Kohli, and Rob Fergus · 2012
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Image guided depth upsampling using anisotropic total generalized variation
David Ferstl, Christian Reinbacher, Rene Ranftl, Matthias Rüther, and Horst Bischof · 2013
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Learning joint intensity-depth sparse representations
Ivana Tosic and Sarah Drewes · 2014
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Color-guided depth recovery from rgb-d data using an adaptive autoregressive model
Jingyu Yang, Xinchen Ye, Kun Li, Chunping Hou, and Yao Wang · 2014
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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U-net: Convolutional networks for biomedical image segmentation
Olaf Ronneberger, Philipp Fischer, and Thomas Brox · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Gaussian error linear units (gelus)
Dan Hendrycks and Kevin Gimpel · 2016
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Fractalnet: Ultra-deep neural networks without residuals
Gustav Larsson, Michael Maire, and Gregory Shakhnarovich · 2016
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Structure-from-motion revisited
Johannes L Schonberger and Jan-Michael Frahm · 2016
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Learning affinity via spatial propagation networks
Sifei Liu, Shalini De Mello, Jinwei Gu, Guangyu Zhong, Ming-Hsuan Yang, and Jan Kautz · 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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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Estimating depth from rgb and sparse sensing
Zhao Chen, Vijay Badrinarayanan, Gilad Drozdov, and Andrew Rabinovich · 2018
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In defense of classical image processing: Fast depth completion on the cpu
Jason Ku, Ali Harakeh, and Steven L Waslander · 2018
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 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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Deep depth completion of a single rgb-d image
Yinda Zhang and Thomas Funkhouser · 2018
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Sparse-to-dense: Depth prediction from sparse depth samples and a single image
Fangchang Ma and Sertac Karaman · 2018
Cited alongside, same era.
Learning joint 2d-3d representations for depth completion
Yun Chen, Bin Yang, Ming Liang, and Raquel Urtasun · 2019
Cited alongside, same era.
Learning depth with convolutional spatial propagation network
Xinjing Cheng, Peng Wang, and Ruigang Yang · 2019
Cited alongside, same era.
Confidence propagation through cnns for guided sparse depth regression
Abdelrahman Eldesokey, Michael Felsberg, and Fahad Shahbaz Khan · 2019
Cited alongside, same era.
Hms-net: Hierarchical multi-scale sparsity-invariant network for sparse depth completion
Zixuan Huang, Junming Fan, Shenggan Cheng, Shuai Yi, Xiaogang Wang, and Hongsheng Li · 2019
Cited alongside, same era.
Depth coefficients for depth completion
Saif Imran, Yunfei Long, Xiaoming Liu, and Daniel Morris · 2019
Non-local spatial propagation network for depth completion
Jinsun Park, Kyungdon Joo, Zhe Hu, Chi-Kuei Liu, and In So Kweon · 2020
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Learning guided convolutional network for depth completion
Jie Tang, Fei-Peng Tian, Wei Feng, Jian Li, and Ping Tan · 2020
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Penet: Towards precise and efficient image guided depth completion
Mu Hu, Shuling Wang, Bin Li, Shiyu Ning, Li Fan, and Xiaojin Gong · 2021
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Learning steering kernels for guided depth completion
Lina Liu, Yiyi Liao, Yue Wang, Andreas Geiger, and Yong Liu · 2021
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Bayesian deep basis fitting for depth completion with uncertainty
Chao Qu, Wenxin Liu, and Camillo J Taylor · 2021
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Rigidfusion: Rgb-d scene reconstruction with rigidly-moving objects
Yu-Shiang Wong, Changjian Li, Matthias Niessner, and Niloy J Mitra · 2021
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Cited alongside, same era.
Joint image filtering with deep convolutional networks
Yijun Li, Jia-Bin Huang, Narendra Ahuja, and Ming-Hsuan Yang · 2019
Cited alongside, same era.
Self-supervised sparse-to-dense: Self-supervised depth completion from lidar and monocular camera
Fangchang Ma, Guilherme Venturelli Cavalheiro, and Sertac Karaman · 2019
Cited alongside, same era.
Learning ambidextrous robot grasping policies
Jeffrey Mahler, Matthew Matl, Vishal Satish, Michael Danielczuk, Bill DeRose, Stephen McKinley, and Ken Goldberg · 2019
Cited alongside, same era.
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
Cited alongside, same era.
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
Cited alongside, same era.
Super-convergence: Very fast training of neural networks using large learning rates
Leslie N Smith and Nicholay Topin · 2019
Cited alongside, same era.
Later among the works it cites.
Penet: Towards precise and efficient image guided depth completion
Mu Hu, Shuling Wang, Bin Li, Shiyu Ning, Li Fan, and Xiaojin Gong · 2021
Later among the works it cites.
Depth completion with twin surface extrapolation at occlusion boundaries
Saif Imran, Xiaoming Liu, and Daniel Morris · 2021
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Adaptive context-aware multi-modal network for depth completion
Shanshan Zhao, Mingming Gong, Huan Fu, and Dacheng Tao · 2021
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Dynamic spatial propagation network for depth completion
Yuankai Lin, Tao Cheng, Qi Zhong, Wending Zhou, and Hua Yang · 2022
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Graphcspn: Geometry-aware depth completion via dynamic gcns
Xin Liu, Xiaofei Shao, Bo Wang, Yali Li, and Shengjin Wang · 2022
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Guideformer: Transformers for image guided depth completion
Kyeongha Rho, Jinsung Ha, and Youngjung Kim · 2022
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Rignet: Repetitive image guided network for depth completion
Zhiqiang Yan, Kun Wang, Xiang Li, Zhenyu Zhang, Jun Li, and Jian Yang · 2022
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Dynamic spatial propagation network for depth completion
Yuankai Lin, Tao Cheng, Qi Zhong, Wending Zhou, and Hua Yang · 2022
Later among the works it cites.
Graphcspn: Geometry-aware depth completion via dynamic gcns
Xin Liu, Xiaofei Shao, Bo Wang, Yali Li, and Shengjin Wang · 2022
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Rignet: Repetitive image guided network for depth completion
Zhiqiang Yan, Kun Wang, Xiang Li, Zhenyu Zhang, Jun Li, and Jian Yang · 2022
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Sparsity agnostic depth completion
Andrea Conti, Matteo Poggi, and Stefano Mattoccia · 2023
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Lrru: Long-short range recurrent updating networks for depth completion
Yufei Wang, Bo Li, Ge Zhang, Qi Liu, Tao Gao, and Yuchao Dai · 2023
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Completionformer: Depth completion with convolutions and vision transformers
Youmin Zhang, Xianda Guo, Matteo Poggi, Zheng Zhu, Guan Huang, and Stefano Mattoccia · 2023
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Bev@ dc: Bird’s-eye view assisted training for depth completion
Wending Zhou, Xu Yan, Yinghong Liao, Yuankai Lin, Jin Huang, Gangming Zhao, Shuguang Cui, and Zhen Li · 2023
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Lrru: Long-short range recurrent updating networks for depth completion
Yufei Wang, Bo Li, Ge Zhang, Qi Liu, Tao Gao, and Yuchao Dai · 2023
Later among the works it cites.
Completionformer: Depth completion with convolutions and vision transformers
Youmin Zhang, Xianda Guo, Matteo Poggi, Zheng Zhu, Guan Huang, and Stefano Mattoccia · 2023
Later among the works it cites.
Bev@ dc: Bird’s-eye view assisted training for depth completion
Wending Zhou, Xu Yan, Yinghong Liao, Yuankai Lin, Jin Huang, Gangming Zhao, Shuguang Cui, and Zhen Li · 2023
Later among the works it cites.