This study addresses the challenging problem of active view selection and
uncertainty quantification within the domain of Radiance Fields. Neural
Radiance Fields (NeRF) have greatly advanced image rendering and
reconstruction, but the limited availability of 2D images poses uncertainties
stemming from occlusions, depth ambiguities, and imaging errors. Efficiently
selecting informative views becomes crucial, and quantifying NeRF model
uncertainty presents intricate challenges. Existing approaches either depend on
model architecture or are based on assumptions regarding density distributions
that are not generally applicable. By leveraging Fisher Information, we
efficiently quantify observed information within Radiance Fields without ground
truth data. This can be used for the next best view selection and pixel-wise
uncertainty quantification. Our method overcomes existing limitations on model
architecture and effectiveness, achieving state-of-the-art results in both view
selection and uncertainty quantification, demonstrating its potential to
advance the field of Radiance Fields. Our method with the 3D Gaussian Splatting
backend could perform view selections at 70 fps.

通过利用 Fisher 信息，本研究提出了一种在没有地面真实数据的情况下，有效地量化 Radiance Fields 中的观察信息，用于最佳视角选择和像素级的不确定性量化，克服了现有模型架构和有效性方面的限制，在视角选择和不确定性量化方面取得了最先进的结果，并展示了推动 Radiance Fields 领域发展的潜力。