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Depth estimation from a single image is an important task that can be applied to various fields in computer vision, and has grown rapidly with the development of convolutional neural networks.
Make3d: Learning 3d scene structure from a single still image
Ashutosh Saxena, Min Sun, and Andrew Y Ng · 2008
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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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Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers
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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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Unsupervised cnn for single view depth estimation: Geometry to the rescue
Ravi Garg, Vijay Kumar BG, Gustavo Carneiro, and Ian Reid · 2016
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Understanding the effective receptive field in deep convolutional neural networks
Wenjie Luo, Yujia Li, Raquel Urtasun, and Richard Zemel · 2016
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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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Deep ordinal regression network for monocular depth estimation
Huan Fu, Mingming Gong, Chaohui Wang, Kayhan Batmanghelich, and Dacheng Tao · 2018
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Benchmarking neural network robustness to common corruptions and perturbations
Dan Hendrycks and Thomas Dietterich · 2018
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Evaluation of cnn-based single-image depth estimation methods
Tobias Koch, Lukas Liebel, Friedrich Fraundorfer, and Marco Korner · 2018
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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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How do neural networks see depth in single images?
Tom van Dijk and Guido de Croon · 2019
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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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Guiding monocular depth estimation using depth-attention volume
Lam Huynh, Phong Nguyen-Ha, Jiri Matas, Esa Rahtu, and Janne Heikkilä · 2020
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Leveraging contextual information for monocular depth estimation
Doyeon Kim, Sihaeng Lee, Janghyeon Lee, and Junmo Kim · 2020
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Aced: Accurate and edge-consistent monocular depth estimation
Kunal Swami, Prasanna Vishnu Bondada, and Pankaj Kumar Bajpai · 2020
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Rethinking data augmentation for image super-resolution: A comprehensive analysis and a new strategy
Jaejun Yoo, Namhyuk Ahn, and Kyung-Ah Sohn · 2020
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Adabins: Depth estimation using adaptive bins
Shariq Farooq Bhat, Ibraheem Alhashim, and Peter Wonka · 2021
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Simple copy-paste is a strong data augmentation method for instance segmentation
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Sharpnet: Fast and accurate recovery of occluding contours in monocular depth estimation
Michael Ramamonjisoa and Vincent Lepetit · 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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Cutmix: Regularization strategy to train strong classifiers with localizable features
Sangdoo Yun, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, Junsuk Choe, and Youngjoon Yoo · 2019
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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 · 2020
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Golnaz Ghiasi, Yin Cui, Aravind Srinivas, Rui Qian, Tsung-Yi Lin, Ekin D Cubuk, Quoc V Le, and Barret Zoph · 2021
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Cutdepth: Edge-aware data augmentation in depth estimation
Yasunori Ishii and Takayoshi Yamashita · 2021
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Vision transformers for dense prediction
René Ranftl, Alexey Bochkovskiy, and Vladlen Koltun · 2021
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Segformer: Simple and efficient design for semantic segmentation with transformers
Enze Xie, Wenhai Wang, Zhiding Yu, Anima Anandkumar, Jose M Alvarez, and Ping Luo · 2021
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