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Despite the widespread deployment of outdoor cameras, their potential forautomated analysis remains largely untapped due, in part, to calibrationchallenges. The absence of precise camera calibration data, including intrinsicand extrinsic parameters, hinders accurate real-world distance measurementsfrom captured videos. To address this, we present a scalable framework thatutilizes street-level imagery to reconstruct a metric 3D model, facilitatingprecise calibration of in-the-wild traffic cameras. Notably, our frameworkachieves 3D scene reconstruction and accurate localization of over 100 globaltraffic cameras and is scalable to any camera with sufficient street-levelimagery. For evaluation, we introduce a dataset of 20 fully calibrated trafficcameras, demonstrating our method's significant enhancements over existingautomatic calibration techniques. Furthermore, we highlight our approach'sutility in traffic analysis by extracting insights via 3D vehiclereconstruction and speed measurement, thereby opening up the potential of usingoutdoor cameras for automated analysis.

Toward Planet-Wide Traffic Camera Calibration