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Found 5 papers, 1 papers with code
Estimating 3D Uncertainty Field: Quantifying Uncertainty for Neural Radiance Fields
To quantify the uncertainty on the learned surface, we model a stochastic radiance field.
Conditional-Flow NeRF: Accurate 3D Modelling with Reliable Uncertainty Quantification
For this purpose, our method learns a distribution over all possible radiance fields modelling which is used to quantify the uncertainty associated with the modelled scene.
Stochastic Neural Radiance Fields: Quantifying Uncertainty in Implicit 3D Representations
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.
Prediction stability as a criterion in active learning
Recent breakthroughs made by deep learning rely heavily on large number of annotated samples.
PartsNet: A Unified Deep Network for Automotive Engine Precision Parts Defect Detection
Defect detection is a basic and essential task in automatic parts production, especially for automotive engine precision parts.
