We present a framework that integrates ground-level imagery with Airborne Laser Scanning (ALS) point clouds. While the Visual Geometry Grounded Transformer (VGGT) enables dense geometry estimation from uncalibrated images, its application is limited by non-metric results and high GPU requirements. By leveraging publicly available, georeferenced ALS point clouds as an external metric constraint, our system restores absolute scale and global coordinates without requiring high-grade GNSS/INS or expensive on-board LiDAR systems. We introduce a confidence-weighted Sim (3) registration algorithm that utilizes a learned confidence mask to filter out unreliable points in dense street-level reconstructions. Experimental evaluations conducted on large-scale urban datasets demonstrate the average check point errors of 0.77 meters in Hong Kong dataset and 0.69 meters in Wuhan dataset, showing great potentials of feed-forward models in large-scale outdoor dense mapping.