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

• URL: http://arxiv.org/abs/2501.01421v1
• Date: Thu, 02 Jan 2025 18:59:08 GMT
• Title: R-SCoRe: Revisiting Scene Coordinate Regression for Robust Large-Scale Visual Localization
• Authors: Xudong Jiang, Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys,
• Abstract summary: We introduce a covisibility graph-based global encoding learning and data augmentation strategy. 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.
• Score: 66.87005863868181
• License:
• Abstract: 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 10$\times$ more accurate than previous SCR methods with similar map sizes and require at least 5$\times$ smaller map sizes than any other SCR method while still delivering superior accuracy. Code will be available at: https://github.com/cvg/scrstudio .

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