R-SCoRe: Revisiting Scene Coordinate Regression for Robust Large-Scale Visual Localization

Xudong Jiang, Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys; Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR), 2025, pp. 11536-11546

Learning-based visual localization methods that use scene coordinate regression (SCR) offer the advantage of smaller map sizes. However, on datasets with complex illumination changes or image-level ambiguities, it remains a less robust alternative to feature matching methods. This work aims to close the gap. We introduce a covisibility graph-based global encoding learning and data augmentation strategy, along with a depth-adjusted reprojection loss to facilitate implicit triangulation. Additionally, we revisit the network architecture and local feature extraction module. Our method achieves state-of-the-art on challenging large-scale datasets without relying on network ensembles or 3D supervision. On Aachen Day-Night, we are 10x more accurate than previous SCR methods with similar map sizes and require at least 5x smaller map sizes than any other SCR method while still delivering superior accuracy. Code is available at: https://github.com/cvg/scrstudio.