  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.

Computer Vision and Pattern Recognition