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Monocular depth estimation (MDE) is a fundamental topic of geometric computer vision and a core technique for many downstream applications.
Learning depth from single monocular images
Ashutosh Saxena, Sung H Chung, Andrew Y Ng, et al · 2005
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Deep sparse rectifier neural networks
Xavier Glorot, Antoine Bordes, and Yoshua Bengio · 2011
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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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Vision meets robotics: The kitti dataset
Andreas Geiger, Philip Lenz, Christoph Stiller, and Raquel Urtasun · 2013
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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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Sun rgb-d: A rgb-d scene understanding benchmark suite
Shuran Song, Samuel P Lichtenberg, and Jianxiong Xiao · 2015
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2015
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Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network
Wenzhe Shi, Jose Caballero, Ferenc Huszár, Johannes Totz, Andrew P Aitken, Rob Bishop, Daniel Rueckert, and Zehan Wang · 2016
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Deeper depth prediction with fully convolutional residual networks
Iro Laina, Christian Rupprecht, Vasileios Belagiannis, Federico Tombari, and Nassir Navab · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Estimating depth from monocular images as classification using deep fully convolutional residual networks
Yuanzhouhan Cao, Zifeng Wu, and Chunhua Shen · 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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Sparsity invariant cnns
Jonas Uhrig, Nick Schneider, Lukas Schneider, Uwe Franke, Thomas Brox, and Andreas Geiger · 2017
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Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
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Orb-slam2: An open-source slam system for monocular, stereo, and rgb-d cameras
Raul Mur-Artal and Juan D Tardós · 2017
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Deep ordinal regression network for monocular depth estimation
Huan Fu, Mingming Gong, Chaohui Wang, Kayhan Batmanghelich, and Dacheng Tao · 2018
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From big to small: Multi-scale local planar guidance for monocular depth estimation
Jin Han Lee, Myung-Kyu Han, Dong Wook Ko, and Il Hong Suh · 2019
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Soft labels for ordinal regression
Raul Diaz and Amit Marathe · 2019
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Enforcing geometric constraints of virtual normal for depth prediction
Wei Yin, Yifan Liu, Chunhua Shen, and Youliang Yan · 2019
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Efficientnet: Rethinking model scaling for convolutional neural networks
Mingxing Tan and Quoc Le · 2019
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Structure-aware residual pyramid network for monocular depth estimation
Xiaotian Chen, Xuejin Chen, and Zheng-Jun Zha · 2019
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Guiding monocular depth estimation using depth-attention volume
Raft-3d: Scene flow using rigid-motion embeddings
Zachary Teed and Jia Deng · 2021
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Patch-wise attention network for monocular depth estimation
Sihaeng Lee, Janghyeon Lee, Byungju Kim, Eojindl Yi, and Junmo Kim · 2021
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Bidirectional attention network for monocular depth estimation
Shubhra Aich, Jean Marie Uwabeza Vianney, Md Amirul Islam, and Mannat Kaur Bingbing Liu · 2021
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Vip-deeplab: Learning visual perception with depth-aware video panoptic segmentation
Siyuan Qiao, Yukun Zhu, Hartwig Adam, Alan Yuille, and Liang-Chieh Chen · 2021
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Neural window fully-connected crfs for monocular depth estimation
Weihao Yuan, Xiaodong Gu, Zuozhuo Dai, Siyu Zhu, and Ping Tan · 2022
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Binsformer: Revisiting adaptive bins for monocular depth estimation
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Lam Huynh, Phong Nguyen-Ha, Jiri Matas, Esa Rahtu, and Janne Heikkilä · 2020
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Self-supervised monocular trained depth estimation using self-attention and discrete disparity volume
Adrian Johnston and Gustavo Carneiro · 2020
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Raft: Recurrent all-pairs field transforms for optical flow
Zachary Teed and Jia Deng · 2020
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Adabins: Depth estimation using adaptive bins
Shariq Farooq Bhat, Ibraheem Alhashim, and Peter Wonka · 2021
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Transformer-based attention networks for continuous pixel-wise prediction
Guanglei Yang, Hao Tang, Mingli Ding, Nicu Sebe, and Elisa Ricci · 2021
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Adaptive surface normal constraint for depth estimation
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2021
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Zhenyu Li, Xuyang Wang, Xianming Liu, and Junjun Jiang · 2022
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Towards comprehensive monocular depth estimation: Multiple heads are better than one
Shuwei Shao, Ran Li, Zhongcai Pei, Zhong Liu, Weihai Chen, Wentao Zhu, Xingming Wu, and Baochang Zhang · 2022
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Localbins: Improving depth estimation by learning local distributions
Shariq Farooq Bhat, Ibraheem Alhashim, and Peter Wonka · 2022
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Uncertainty quantification in depth estimation via constrained ordinal regression
Dongting Hu, Liuhua Peng, Tingjin Chu, Xiaoxing Zhang, Yinian Mao, Howard Bondell, and Mingming Gong · 2022
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Itermvs: iterative probability estimation for efficient multi-view stereo
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Swin transformer v2: Scaling up capacity and resolution
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P3depth: Monocular depth estimation with a piecewise planarity prior
Vaishakh Patil, Christos Sakaridis, Alexander Liniger, and Luc Van Gool · 2022
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Urcdc-depth: Uncertainty rectified cross-distillation with cutflip for monocular depth estimation
Shuwei Shao, Zhongcai Pei, Weihai Chen, Ran Li, Zhong Liu, and Zhengguo Li · 2023
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Nddepth: Normal-distance assisted monocular depth estimation
Shuwei Shao, Zhongcai Pei, Weihai Chen, Xingming Wu, and Zhengguo Li · 2023
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Attention attention everywhere: Monocular depth prediction with skip attention
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Dro: Deep recurrent optimizer for video to depth
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