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Monocular depth estimation is an ongoing challenge in computer vision.
Depth estimation from image structure
Antonio Torralba and Aude Oliva · 2002
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Depth estimation using monocular and stereo cues
Ashutosh Saxena, Jamie Schulte, Andrew Y Ng, et al · 2007
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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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Measuring perceived depth in natural images and study of its relation with monocular and binocular depth cues
Pierre Lebreton, Alexander Raake, Marcus Barkowsky, and Patrick Le Callet · 2014
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Grad-cam: Visual explanations from deep networks via gradient-based localization
Ramprasaath R Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh, and Dhruv Batra · 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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Revisiting single image depth estimation: Toward higher resolution maps with accurate object boundaries
Junjie Hu, Mete Ozay, Yan Zhang, and Takayuki Okatani · 2019
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Efficientnet: Rethinking model scaling for convolutional neural networks
Mingxing Tan and Quoc Le · 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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Quantifying attention flow in transformers
Samira Abnar and Willem Zuidema · 2020
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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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Cnn explainer: learning convolutional neural networks with interactive visualization
Zijie J Wang, Robert Turko, Omar Shaikh, Haekyu Park, Nilaksh Das, Fred Hohman, Minsuk Kahng, and Duen Horng Polo Chau · 2020
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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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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Depthformer: Multiscale vision transformer for monocular depth estimation with global local information fusion
Ashutosh Agarwal and Chetan Arora · 2022
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Global-local path networks for monocular depth estimation with vertical cutdepth
Doyeon Kim, Woonghyun Ka, Pyungwhan Ahn, Donggyu Joo, Sehwan Chun, and Junmo Kim · 2022
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Adabins: Depth estimation using adaptive bins
Shariq Farooq Bhat, Ibraheem Alhashim, and Peter Wonka · 2021
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Transformer interpretability beyond attention visualization
Hila Chefer, Shir Gur, and Lior Wolf · 2021
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Swin-depth: Using transformers and multi-scale fusion for monocular-based depth estimation
Zeyu Cheng, Yi Zhang, and Chengkai Tang · 2021
Cited alongside, same era.
Escaping the big data paradigm with compact transformers
Ali Hassani, Steven Walton, Nikhil Shah, Abulikemu Abuduweili, Jiachen Li, and Humphrey Shi · 2021
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Swin transformer: Hierarchical vision transformer using shifted windows
Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo · 2021
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Visualization of convolutional neural networks for monocular depth estimation
Junjie Hu, Yan Zhang, and Takayuki Okatani
Cited in the paper.
Nddepth: Normal-distance assisted monocular depth estimation
Shuwei Shao, Zhongcai Pei, Weihai Chen, Xingming Wu, and Zhengguo Li
Cited in the paper.
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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Attention attention everywhere: Monocular depth prediction with skip attention
Ashutosh Agarwal and Chetan Arora · 2023
Closest in time.
Dwinformer: Dual window transformers for end-to-end monocular depth estimation
Md Awsafur Rahman and Shaikh Anowarul Fattah · 2023
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Dongseok Shim and H Jin Kim · 2023
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Lite-mono: A lightweight cnn and transformer architecture for self-supervised monocular depth estimation
Ning Zhang, Francesco Nex, George Vosselman, and Norman Kerle · 2023
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