Bayesian NeRF: Quantifying Uncertainty with Volume Density in Neural Radiance Fields

arXiv:2404.06727v1 Announce Type: new
Abstract: We present the Bayesian Neural Radiance Field (NeRF), which explicitly quantifies uncertainty in geometric volume structures without the need for additional networks, making it adept for challenging observations and uncontrolled images. NeRF diverges from traditional geometric methods by offering an enriched scene representation, rendering color and density in 3D space from various viewpoints. However, NeRF encounters limitations in relaxing uncertainties by using geometric structure information, leading to inaccuracies in interpretation under insufficient real-world observations. Recent …

abstract adept arxiv bayesian cs.cv fields images making nerf networks neural radiance field neural radiance fields rendering representation type uncertainty