mlsanityData to 7 Oct 2026

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

arXiv 2501.01421 ↗First posted 2 Jan 2025cs.CVcited by 16

Not on the map. Why?

Abstract (arXiv)

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× more accurate than previous SCR methods with similar map sizes and require at least 5× smaller map sizes than any other SCR method while still delivering superior accuracy. Code is available at: https://github.com/cvg/scrstudio .

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Builds on 55 of its 86 references that are in the map. First posted on arXiv 2 Jan 2025.

Builds on

55 of 86 references.

References · 55

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38 more
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50 most cited of 55 All 55 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 6; 2026: 10.

16 papers.

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