4 citations · 7 across the 3 of their papers we have counts for
3 papers
Estimating 3D Uncertainty Field: Quantifying Uncertainty for Neural Radiance Fields
Jianxiong Shen, Ruijie Ren, Adria Ruiz +1
Current methods based on Neural Radiance Fields (NeRF) significantly lack the capacity to quantify uncertainty in their predictions, particularly on the unseen space including the…
Conditional-Flow NeRF: Accurate 3D Modelling with Reliable Uncertainty Quantification
Jianxiong Shen, Antonio Agudo, Francesc Moreno-Noguer +1
A critical limitation of current methods based on Neural Radiance Fields (NeRF) is that they are unable to quantify the uncertainty associated with the learned appearance and geome…
Stochastic Neural Radiance Fields: Quantifying Uncertainty in Implicit 3D Representations
Jianxiong Shen, Adria Ruiz, Antonio Agudo +1
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 estim…