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Self-supervised depth estimation from monocular cameras in diverse outdoor conditions, such as daytime, rain, and nighttime, is challenging due to the difficulty of learning universal representations and the severe lack of labeled real-world adverse data.
Deep residual learning for image recognition
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W. Maddern, G. Pascoe, C. Linegar, and P. Newman · 2016
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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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Unsupervised learning of depth and ego-motion from video
Tinghui Zhou, Matthew Brown, Noah Snavely, and David G. Lowe · 2017
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Unpaired image-to-image translation using cycle-consistent adversarial networkss
Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros · 2017
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Holger Caesar, Varun Bankiti, Alex H. Lang, Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom · 2019
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Vitor Guizilini, Rares Ambrus, Sudeep Pillai, Allan Raventos, and Adrien Gaidon · 2020
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Defeat-net: General monocular depth via simultaneous unsupervised representation learning
Jaime Spencer, Richard Bowden, and Simon Hadfield · 2020
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Unsupervised monocular depth estimation for night-time images using adversarial domain feature adaptation
Madhu Vankadari, Sourav Garg, Anima Majumder, Swagat Kumar, and Ardhendu Behera · 2020
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Unsupervised scale-consistent depth learning from video
Jia-Wang Bian, Huangying Zhan, Naiyan Wang, Zhichao Li, Le Zhang, Chunhua Shen, Ming-Ming Cheng, and Ian Reid · 2021
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Steps: Joint self-supervised nighttime image enhancement and depth estimation
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Diffusion models for monocular depth estimation: Overcoming challenging conditions
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Prodepth: Boosting self-supervised multi-frame monocular depth with probabilistic fusion
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Towards better text-to-image generation alignment via attention modulation
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Exploring the common appearance-boundary adaptation for nighttime optical flow
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