Estimating camera poses jointly from aerial and ground imagery remains difficult because large viewpoint changes reduce overlap, alter appearance, and weaken the geometric assumptions relied on by both classical photogrammetry and recent AI-based reconstruction models. This paper presents a controlled comparison between a classic photogrammetric approach represented by COLMAP and a cross-view fine-tuned end-to-end model based on Dust3R. Tests are carried out on a London building scene containing 10 aerial and 29 ground images. Fine-tuned Dust3R reconstructs the full image set, whereas COLMAP successfully registers 24 ground-level images. Because both reconstructions are defined only up to an unknown similarity transform and no ground-truth poses are available, we evaluate the shared subset through 7-DoF similarity transformation analysis rather than direct metric pose errors. After transformation, the translation RMSE of the shared camera centres is 10.0% of the reconstructed scene diagonal in the fine-tuned Dust3R coordinate frame. We further compare pairwise geometric support using a unified fundamental-matrix RANSAC evaluation over 406 image pairs. The AI-based pipeline achieves substantially higher inlier ratios than photogrammetric pipeline under the same verification settings, indicating more successful cross-view orientation. The study contributes a clearer evaluation protocol for mixed aerial-ground pose estimation without ground truth, together with an empirical analysis of robustness, alignment behaviour, and current limitations of both pipelines.