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Neural Radiance Fields (NeRFs) have shown promise in applications like view synthesis and depth estimation, but learning from multiview images faces inherent uncertainties.
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An evaluation of the equilibrium calculations within acidification models: the effect of uncertainty in measured chemical components
William D Schecher and Charles T Driscoll · 1988
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Predicting uncertainty in forecasts of weather and climate
Tim N Palmer · 2000
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Masamichi Sugihara, Brian Wyvill, and Ryan Schmidt · 2010
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Uncertainty quantification: theory, implementation, and applications
Ralph C Smith · 2013
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Sivaram Ambikasaran, Michael O’Neil, and Karan Raj Singh · 2014
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Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra · 2015
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Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2016
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Kaan Yücer, Alexander Sorkine-Hornung, and Oliver Disney · 2016
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What uncertainties do we need in bayesian deep learning for computer vision?
Alex Kendall and Yarin Gal · 2017
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Anastasia Tkach, Andrea Tagliasacchi, Edoardo Remelli, Mark Pauly, and Andrew Fitzgibbon · 2017
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Uncertainty estimates and multi-hypotheses networks for optical flow
Eddy Ilg, Özgün Çiçek, Silvio Galesso, Aaron Klein, Osama Makansi, Frank Hutter, and Thomas Brox · 2018
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A scalable laplace approximation for neural networks
Hippolyt Ritter, Aleksandar Botev, and David Barber · 2018
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Piotr Bojanowski, Armand Joulin, David Lopez-Paz, and Arthur Szlam · 2019
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Non-linear sphere tracing for rendering deformed signed distance fields
Dario Seyb, Alec Jacobson, Derek Nowrouzezahrai, and Wojciech Jarosz · 2019
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NeRF in the Wild: Neural radiance fields for unconstrained photo collections
Ricardo Martin-Brualla, Noha Radwan, Mehdi Sajjadi, Jonathan T. Barron, Alexey Dosovitskiy, and Daniel Duckworth · 2020
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Stochastic segmentation networks: Modelling spatially correlated aleatoric uncertainty
Miguel Monteiro, Loïc Le Folgoc, Daniel Coelho de Castro, Nick Pawlowski, Bernardo Marques, Konstantinos Kamnitsas, Mark van der Wilk, and Ben Glocker · 2020
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Deformable neural radiance fields
Test-time data augmentation for estimation of heteroscedastic aleatoric uncertainty in deep neural networks
Murat Seckin Ayhan and Philipp Berens · 2022
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Probnerf: Uncertainty-aware inference of 3d shapes from 2d images
Matthew D. Hoffman, Tuan Anh Le, Pavel Sountsov, Christopher Suter, Ben Lee, Vikash K. Mansinghka, and Rif A. Saurous · 2022
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Instant neural graphics primitives with a multiresolution hash encoding
Thomas Müller, Alex Evans, Christoph Schied, and Alexander Keller · 2022
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Activenerf: Learning where to see with uncertainty estimation
Xuran Pan, Zihang Lai, Shiji Song, and Gao Huang · 2022
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Lolnerf: Learn from one look
Daniel Rebain, Mark Matthews, Kwang Moo Yi, Dmitry Lagun, and Andrea Tagliasacchi · 2022
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Conditional-flow nerf: Accurate 3d modelling with reliable uncertainty quantification
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Keunhong Park, Utkarsh Sinha, Jonathan T. Barron, Sofien Bouaziz, Dan B. Goldman, Steven M. Seitz, and Ricardo Martin-Brualla · 2020
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Kyle Wilson and Scott Wehrwein · 2020
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A review of uncertainty quantification in deep learning: Techniques, applications and challenges
Moloud Abdar, Farhad Pourpanah, Sadiq Hussain, Dana Rezazadegan, Li Liu, Mohammad Ghavamzadeh, Paul Fieguth, Abbas Khosravi, U Rajendra Acharya, Vladimir Makarenkov, and Saeid Nahavandi · 2021
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Kashyap Chitta, Aditya Prakash, and Andreas Geiger · 2021
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Erik Daxberger, Agustinus Kristiadi, Alexander Immer, Runa Eschenhagen, Matthias Bauer, and Philipp Hennig · 2021
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Neu-nbv: Next best view planning using uncertainty estimation in image-based neural rendering
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