  Despite the widespread deployment of outdoor cameras, their potential for
automated analysis remains largely untapped due, in part, to calibration
challenges. The absence of precise camera calibration data, including intrinsic
and extrinsic parameters, hinders accurate real-world distance measurements
from captured videos. To address this, we present a scalable framework that
utilizes street-level imagery to reconstruct a metric 3D model, facilitating
precise calibration of in-the-wild traffic cameras. Notably, our framework
achieves 3D scene reconstruction and accurate localization of over 100 global
traffic cameras and is scalable to any camera with sufficient street-level
imagery. For evaluation, we introduce a dataset of 20 fully calibrated traffic
cameras, demonstrating our method's significant enhancements over existing
automatic calibration techniques. Furthermore, we highlight our approach's
utility in traffic analysis by extracting insights via 3D vehicle
reconstruction and speed measurement, thereby opening up the potential of using
outdoor cameras for automated analysis.
