Leveraging Neural Radiance Fields for Uncertainty-Aware Visual Localization

TL;DR

This paper introduces a novel approach combining Neural Radiance Fields with scene coordinate regression, utilizing uncertainty estimation for efficient view selection to improve visual localization accuracy.

Contribution

It proposes a method that leverages NeRF-generated data with uncertainty modeling to enhance SCR, reducing data redundancy and improving localization performance.

Findings

Improved localization accuracy with fewer training samples.

Effective view selection based on pixel-level uncertainty.

Enhanced data efficiency in visual localization tasks.

Abstract

As a promising fashion for visual localization, scene coordinate regression (SCR) has seen tremendous progress in the past decade. Most recent methods usually adopt neural networks to learn the mapping from image pixels to 3D scene coordinates, which requires a vast amount of annotated training data. We propose to leverage Neural Radiance Fields (NeRF) to generate training samples for SCR. Despite NeRF's efficiency in rendering, many of the rendered data are polluted by artifacts or only contain minimal information gain, which can hinder the regression accuracy or bring unnecessary computational costs with redundant data. These challenges are addressed in three folds in this paper: (1) A NeRF is designed to separately predict uncertainties for the rendered color and depth images, which reveal data reliability at the pixel level. (2) SCR is formulated as deep evidential learning with epistemic uncertainty, which is used to evaluate information gain and scene coordinate quality. (3) Based on the three arts of uncertainties, a novel view selection policy is formed that significantly improves data efficiency. Experiments on public datasets demonstrate that our method could select the samples that bring the most information gain and promote the performance with the highest efficiency.