The Nerfect Match: Exploring NeRF Features for Visual Localization

Abstract

In this work, we propose the use of Neural Radiance Fields () as a scene representation for visual localization. Recently, has been employed to enhance pose regression and scene coordinate regression models by augmenting the training database, providing auxiliary supervision through rendered images, or serving as an iterative refinement module. We extend its recognized advantages – its ability to provide a compact scene representation with realistic appearances and accurate geometry – by exploring the potential of ’s internal features in establishing precise 2D-3D matches for localization. To this end, we conduct a comprehensive examination of ’s implicit knowledge, acquired through view synthesis, for matching under various conditions. This includes exploring different matching network architectures, extracting encoder features at multiple layers, and varying training configurations. Significantly, we introduce , an advanced 2D-3D matching function that capitalizes on the internal knowledge of learned via view synthesis. Our evaluation of on standard localization benchmarks, within a structure-based pipeline, achieves competitive results for localization performance on Cambridge Landmarks. We will release all models and code.