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.

基于学习的不完整场景几何构建的 3D 不确定性场，可以明确识别未见区域，推理高不确定性，并适用于实际机器人任务。