Research
VCURF: Virtual Camera-based Uncertainty of Radiance Fields
arXiv:2610.04076v1 Announce Type: new Abstract: Radiance fields, implemented with either implicit (NeRF) or explicit (Gaussian Splatting) representations, are advancing the state of the art in novel v
arXiv:2610.04076v1 Announce Type: new Abstract: Radiance fields, implemented with either implicit (NeRF) or explicit (Gaussian Splatting) representations, are advancing the state of the art in novel view synthesis at a rapid pace. Even though the rendered views they generate are often compelling, they are not free of errors. In this paper, we propose a new approach for pixel-wise uncertainty quantification based on measuring the inconsistencies among renderings by the radiance field model in virtual cameras sampled near the target viewpoint. We named our approach VCURF for Virtual Camera-based Uncertainty of Radiance Fields. VCURF treats the radiance field model as a black box, only assuming that it is capable of rendering color and depth on demand. This property makes our approach applicable to both NeRF and GS models without any modification. Our experiments on a combination of datasets, radiance field models and baselines demonstrate VCURF's effectiveness in pixel-wise uncertainty estimation. We conclude the paper with findings that question the way view selection is tackled by the majority of the current literature.
Related
- PCM-NeRF: Probabilistic Camera Modeling for Neural Radiance Fields under Pose Uncertainty
- From Explicit References to Scene Manifolds: Distributional Fidelity and Realism for Radiance Field Quality Assessment
- SteadySplats: Resampling of Low-Variance Gaussians for High-Fidelity Stochastic Rendering
Source: arXiv cs.CV | 2026-10-06