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Monocular depth estimation is scale-ambiguous, and thus requires scale supervision to produce metric predictions.
On information and sufficiency
S. Kullback and R. A. Leibler · 1951
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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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Indoor segmentation and support inference from rgbd images
Pushmeet Kohli Nathan Silberman, Derek Hoiem 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 using a multi-scale deep network
David Eigen, Christian Puhrsch, and Rob Fergus · 2014
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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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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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Variational inference: A review for statisticians
David M. Blei, Alp Kucukelbir, and Jon D. McAuliffe · 2017
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Unsupervised monocular depth estimation with left-right consistency
Clément Godard, Oisin Mac Aodha, and Gabriel J Brostow · 2017
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Sparsity invariant cnns
J. Uhrig, N. Schneider, L. Schneider, U. Franke, T. Brox, and A. Geiger · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin · 2017
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Unsupervised learning of depth and ego-motion from video
Tinghui Zhou, Matthew Brown, Noah Snavely, and David G Lowe · 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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CAM-Convs: Camera-Aware Multi-Scale Convolutions for Single-View Depth
Jose M. Facil, Benjamin Ummenhofer, Huizhong Zhou, Luis Montesano, Thomas Brox, and Javier Civera · 2019
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Digging into self-supervised monocular depth prediction
Clément Godard, Oisin Mac Aodha, Michael Firman, and Gabriel J. Brostow · 2019
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Depth from videos in the wild: Unsupervised monocular depth learning from unknown cameras
Ariel Gordon, Hanhan Li, Rico Jonschkowski, and Anelia Angelova · 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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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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Spigan: Privileged adversarial learning from simulation
Kuan-Hui Lee, German Ros, Jie Li, and Adrien Gaidon · 2019
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Decoupled weight decay regularization, 2019
Ilya Loshchilov and Frank Hutter · 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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Dada: Depth-aware domain adaptation in semantic segmentation
Tuan-Hung Vu, Himalaya Jain, Maxime Bucher, Mathieu Cord, and Patrick Pérez · 2019
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Geometry-aware symmetric domain adaptation for monocular depth estimation
Shanshan Zhao, Huan Fu, Mingming Gong, and Dacheng Tao · 2019
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Mapillary planet-scale depth dataset
Manuel López Antequera, Pau Gargallo, Markus Hofinger, Samuel Rota Bulò, Yubin Kuang, and Peter Kontschieder · 2020
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Unsupervised depth learning in challenging indoor video: Weak rectification to rescue
Jiawang Bian, Huangying Zhan, Naiyan Wang, Tat-Jun Chin, Chunhua Shen, and Ian D. Reid · 2020
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nuscenes: A multimodal dataset for autonomous driving
Holger Caesar, Varun Bankiti, Alex H Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom · 2020
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Putting nerf on a diet: Semantically consistent few-shot view synthesis
Ajay Jain, Matthew Tancik, and Pieter Abbeel · 2021
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Monoindoor: Towards good practice of self-supervised monocular depth estimation for indoor environments
P. Ji, R. Li, B. Bhanu, and Y. Xu · 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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Structdepth: Leveraging the structural regularities for self-supervised indoor depth estimation
Boying Li, Yuan Huang, Zeyu Liu, Danping Zou, and Wenxian Yu · 2021
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Vision transformers for dense prediction
René Ranftl, Alexey Bochkovskiy, and Vladlen Koltun · 2021
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3d packing for self-supervised monocular depth estimation
Vitor Guizilini, Rares Ambrus, Sudeep Pillai, Allan Raventos, and Adrien Gaidon · 2020
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Semantically-guided representation learning for self-supervised monocular depth
Vitor Guizilini, Rui Hou, Jie Li, Rares Ambrus, and Adrien Gaidon · 2020
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Instance adaptive self-training for unsupervised domain adaptation
Jiaqi Zou Ke Mei, Chuang Zhu and Shanghang Zhang · 2020
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Feature-metric loss for self-supervised learning of depth and egomotion
Chang Shu, Kun Yu, Zhixiang Duan, and Kuiyuan Yang · 2020
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Scalability in perception for autonomous driving: Waymo open dataset
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Tartanair: A dataset to push the limits of visual slam
Wenshan Wang, Delong Zhu, Xiangwei Wang, Yaoyu Hu, Yuheng Qiu, Chen Wang, Yafei Hu, Ashish Kapoor, and Sebastian Scherer · 2020
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Unsupervised scene adaptation with memory regularization in vivo
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Self-supervised scale recovery for monocular depth and egomotion estimation
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Self-supervised camera self-calibration from video
Jiading Fang, Igor Vasiljevic, Vitor Guizilini, Rares Ambrus, Greg Shakhnarovich, Adrien Gaidon, and Matthew Walter · 2022
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Multi-frame self-supervised depth with transformers
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Learning optical flow, depth, and scene flow without real-world labels
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Depth field networks for generalizable multi-view scene representation
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Runze Li, Pan Ji, Yi Xu, and Bir Bhanu · 2022
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Binsformer: Revisiting adaptive bins for monocular depth estimation
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Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer
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Do what you can, with what you have: Scale-aware and high quality monocular depth estimation without real world labels
Kunal Swami, Amrit Muduli, Uttam Gurram, and Pankaj Bajpai · 2022
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Surrounddepth: Entangling surrounding views for self-supervised multi-camera depth estimation
Yi Wei, Linqing Zhao, Wenzhao Zheng, Zheng Zhu, Yongming Rao, Guan Huang, Jiwen Lu, and Jie Zhou · 2022
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Toward practical monocular indoor depth estimation
Cho-Ying Wu, Jialiang Wang, Michael Hall, Ulrich Neumann, and Shuochen Su · 2022
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Input-level inductive biases for 3D reconstruction
Wang Yifan, Carl Doersch, Relja Arandjelović, João Carreira, and Andrew Zisserman · 2022
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Attention attention everywhere: Monocular depth prediction with skip attention
Ashutosh Agarwal and Chetan Arora · 2023
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Zoedepth: Zero-shot transfer by combining relative and metric depth, 2023
Shariq Farooq Bhat, Reiner Birkl, Diana Wofk, Peter Wonka, and Matthias Müller · 2023
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Va-depthnet: A variational approach to single image depth prediction, 2023
Ce Liu, Suryansh Kumar, Shuhang Gu, Radu Timofte, and Luc Van Gool · 2023
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