提升场景坐标回归的鲁棒性，实现在复杂光照下的高精度定位
R-SCoRe: Revisiting Scene Coordinate Regression for Robust Large-Scale Visual Localization
- 基于共视图的全局编码与数据增强策略，增强模型泛化能力
- 在Aachen Day-Night上精度提升10倍，地图尺寸缩小5倍以上
- 无需3D监督或模型集成，适合大规模实时定位场景
基于学习的视觉定位方法中，场景坐标回归（SCR）具有地图体积小的优势。然而，在光照变化复杂或图像存在歧义的场景下，其鲁棒性仍逊于特征匹配方法。本文提出一种基于共视图的全局编码学习与数据增强策略，并引入深度调整的重投影损失以实现隐式三角化。同时重构网络架构与局部特征提取模块。所提方法在无需3D监督或模型集成的前提下，达到当前最优性能。在挑战性的大规模数据集Aachen Day-Night上，相较以往SCR方法精度提升10倍，地图尺寸仅为其他方法的1/5，且保持更高定位精度。代码已开源：https://github.com/cvg/scrstudio。
原文摘要 · Abstract (English)
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 is available at: https://github.com/cvg/scrstudio .
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