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Título: | Stochastic Neural Radiance Fields: Quantifying uncertainty in implicit 3D representations |
Autor: | Shen, Jianxiong CSIC; Ruiz Ovejero, Adrià CSIC; Agudo, Antonio CSIC ORCID ; Moreno-Noguer, Francesc CSIC ORCID | Fecha de publicación: | 2021 | Editor: | Institute of Electrical and Electronics Engineers | Citación: | International Conference on 3D Vision (3DV): 972-981 (2021) | Resumen: | Neural Radiance Fields (NeRF) has become a popular framework for learning implicit 3D representations and addressing different tasks such as novel-view synthesis or depth-map estimation. However, in downstream applications where decisions need to be made based on automatic predictions, it is critical to leverage the confidence associated with the model estimations. Whereas uncertainty quantification is a long-standing problem in Machine Learning, it has been largely overlooked in the recent NeRF literature. In this context, we propose Stochastic Neural Radiance Fields (S-NeRF), a generalization of standard NeRF that learns a probability distribution over all the possible radiance fields modeling the scene. This distribution allows to quantify the uncertainty associated with the scene information provided by the model. S-NeRF optimization is posed as a Bayesian learning problem that is efficiently addressed using the Variational Inference framework. Exhaustive experiments over benchmark datasets demonstrate that S-NeRF is able to provide more reliable predictions and confidence values than generic approaches previously proposed for uncertainty estimation in other domains. | Descripción: | Trabajo presentado en la 9th International Conference on 3D Vision, celebrada online del 1 al 3 de diciembre de 2021 | Versión del editor: | http://dx.doi.org/10.1109/3DV53792.2021.00105 | URI: | http://hdl.handle.net/10261/265783 | DOI: | 10.1109/3DV53792.2021.00105 | Identificadores: | doi: 10.1109/3DV53792.2021.00105 issn: 2475-7888 |
Aparece en las colecciones: | (IRII) Comunicaciones congresos |
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2513-Stochastic-Neural-Radiance-Fields_-Quantifying-Uncertainty-in-Implicit-3D-Representations.pdf | 7,67 MB | Adobe PDF | Visualizar/Abrir |
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