Radiance fields are powerful and, hence, popular models for representing the appearance of complex scenes. Yet, constructing them based on image observations gives rise to ambiguities and uncertainties. We propose a versatile approach for learning Gaussian radiance fields with explicit and fine-grained uncertainty estimates that impose only little additional cost compared to uncertainty-agnostic training. Our key observation is that uncertainties can be modeled as a low-dimensional manifold in the space of radiance field parameters that is highly amenable to Monte Carlo sampling. Importantly, our uncertainties are differentiable and, thus, allow for gradient-based optimization of subsequent captures that optimally reduce ambiguities. We demonstrate state-of-the-art performance on next-best-view planning tasks, including high-dimensional illumination planning for optimal radiance field relighting quality.
@article{arxiv.2409.12661,
title = {Manifold Sampling for Differentiable Uncertainty in Radiance Fields},
author = {Linjie Lyu and Ayush Tewari and Marc Habermann and Shunsuke Saito and Michael Zollhöfer and Thomas Leimkühler and Christian Theobalt},
journal= {arXiv preprint arXiv:2409.12661},
year = {2024}
}