Abstract—Crowd-sourced imagery is increasingly important for urban mapping and visual localization. However, its reliability is limited by GPS inaccuracies and heterogeneous capture condi- tions, including device variability, viewpoint differences, illumi- nation changes, and temporal shifts. In these settings, achieving metric-scale pose estimation remains a central challenge. Deep Learning-based pose estimation models address this problem by learning to estimate the 6-DoF pose using geometric cues between image views and metric supervision during training on large datasets. This encourages spatial consistency and sup- ports generalization across diverse conditions. Recent learning- based architectures, often based on vision transformer encoders, approach the task through unified multi-task frameworks that jointly predict metric depthmaps and 2D–2D correspondences, with relative pose estimated downstream. This thesis evaluates whether such frameworks predict accurate metric depthmaps under domain shifts. Experiments show that, even with scale correction through data-driven fine-tuning with metric supervi- sion, depth predictions from multi-task relative pose estimation models fail to generalize reliably to out-of-domain environments. In contrast, monocular models, trained on significantly larger and more varied datasets, demonstrate strong zero-shot reliability for metric depth prediction. A hybrid pipeline is proposed that combines the geometric consistency of relative pose models with the stable metric cues of monocular models, enabling robust pose estimation in crowd-sourced outdoor environments.