FaVoR: Features via Voxel Rendering for Camera Relocalization

Vincenzo Polizzi, Marco Cannici, Davide Scaramuzza, Jonathan Kelly; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 44-53

Camera relocalization methods range from dense image alignment to direct camera pose regression from a query image. Among these sparse feature matching stands out as an efficient versatile and generally lightweight approach with numerous applications. However feature-based methods often struggle with significant viewpoint and appearance changes leading to matching failures and inaccurate pose estimates. To overcome these limitations we propose a novel approach that leverages a globally sparse but locally dense 3D representation of 2D features. By tracking and triangulating landmarks over a sequence of frames we construct a sparse voxel map optimized to render image patch descriptors observed during tracking. Given an initial pose estimate we first synthesize descriptors from the voxels using volumetric rendering and then perform feature matching to estimate the camera pose. This method enables the generation of descriptors for unseen views enhancing robustness to viewpoint changes. We evaluate our method on the 7-Scenes and Cambridge Landmarks datasets. Our results show that our approach significantly outperforms existing state-of-the-art feature representation techniques in indoor environments achieving up to a 39% improvement in median translation error. Additionally our approach yields comparable results to other methods for outdoor scenes but with lower computational and memory footprints.