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Estimating 3D Uncertainty Field: Quantifying Uncertainty for Neural Radiance Fields
Computer Vision and Pattern Recognition · Submitted 3 Nov 2023
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Our experiments demonstrate that our approach is the only one that can explicitly reason about high uncertainty both on 3D unseen regions and its involved 2D rendered pixels, compared with recent methods.
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The abstract, sentence by sentence
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 occluded and outside scene content. This limitation hinders their extensive applications in robotics, where the reliability of model predictions has to be considered for tasks such as robotic exploration and planning in unknown environments. To address this, we propose a novel approach to estimate a 3D Uncertainty Field based on the learned incomplete scene geometry, which explicitly identifies these unseen regions. By considering the accumulated transmittance along each camera ray, our Uncertainty Field infers 2D pixel-wise uncertainty, exhibiting high values for rays directly casting towards occluded or outside the scene content. To quantify the uncertainty on the learned surface, we model a stochastic radiance field. Our experiments demonstrate that our approach is the only one that can explicitly reason about high uncertainty both on 3D unseen regions and its involved 2D rendered pixels, compared with recent methods. Furthermore, we illustrate that our designed uncertainty field is ideally suited for real-world robotics tasks, such as next-best-view selection.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | 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 occluded and outside scene content. | gap 0.64 | 0.06 | 0.04 | asserted 1.00 |
| s1 | This limitation hinders their extensive applications in robotics, where the reliability of model predictions has to be considered for tasks such as robotic exploration and planning in unknown environments. | background 0.75 | 0.04 | 0.03 | asserted 0.99 |
| s2 | To address this, we propose a novel approach to estimate a 3D Uncertainty Field based on the learned incomplete scene geometry, which explicitly identifies these unseen regions. | contribution 0.98 | 0.84 | 0.13 | asserted 0.99 |
| s3 | By considering the accumulated transmittance along each camera ray, our Uncertainty Field infers 2D pixel-wise uncertainty, exhibiting high values for rays directly casting towards occluded or outside the scene content. | method 0.97 | 0.92 | 0.08 | asserted 1.00 |
| s4 | To quantify the uncertainty on the learned surface, we model a stochastic radiance field. | method 0.98 | 0.92 | 0.04 | asserted 1.00 |
| s5 | Our experiments demonstrate that our approach is the only one that can explicitly reason about high uncertainty both on 3D unseen regions and its involved 2D rendered pixels, compared with recent methods. | result 0.96 | 0.94 | 0.73 | asserted 1.00 |
| s6 | Furthermore, we illustrate that our designed uncertainty field is ideally suited for real-world robotics tasks, such as next-best-view selection. | interpretation 0.72 | 0.87 | 0.19 | asserted 0.99 |
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The paper
Original abstract
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 occluded and outside scene content. This limitation hinders their extensive applications in robotics, where the reliability of model predictions has to be considered for tasks such as robotic exploration and planning in unknown environments. To address this, we propose a novel approach to estimate a 3D Uncertainty Field based on the learned incomplete scene geometry, which explicitly identifies these unseen regions. By considering the accumulated transmittance along each camera ray, our Uncertainty Field infers 2D pixel-wise uncertainty, exhibiting high values for rays directly casting towards occluded or outside the scene content. To quantify the uncertainty on the learned surface, we model a stochastic radiance field. Our experiments demonstrate that our approach is the only one that can explicitly reason about high uncertainty both on 3D unseen regions and its involved 2D rendered pixels, compared with recent methods. Furthermore, we illustrate that our designed uncertainty field is ideally suited for real-world robotics tasks, such as next-best-view selection.
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How the literature uses it
3 arXiv papers cite this in 3 sentences
Papers citing it · 1 sentence without a confident label
Cites it as background 2 papers
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Finally, two similar works, [ref] and [ref] , focus on developing methods that actively estimate high uncertainty in spatial areas that are not covered by the input views, using VI and Ensemble Learning frameworks, respectively.
Modeling uncertainty for Gaussian Splatting · Structure from Motion uncertainty estimation · 0.96
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As for the field of 3D scene representation, a series of works [ref] have focused on quantifying uncertainty in the prediction of NeRF.
Drone-assisted Road Gaussian Splatting with Cross-view Uncertainty · Uncertainty Modeling · 0.98
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