Accuracy Evaluation and Prediction of Single-Image Camera Calibration

This paper proposes an application to statistically predict the accuracy of single-image geometric camera calibration that uses given 2D-3D correspondences. Deriving both camera intrinsics and extrinsics from correspondences between a single image and a 3D shape, is important for the scene analysis when the optical system of the camera is lost, such as in the analyses of traffic accidents. It is unclear whether the single-image calibration will be successful in practice, particularly when the number of 2D-3D correspondences is small, even if we could assign accurate correspondences by manual labor. To this end, we perform a systematic evaluation of the camera parameter accuracy using synthetic environments. Based on the statistics observed during the experiments, our application predicts the calibration accuracy from simple variables (e.g., the area that correspondences could be given). Since the prediction process does not rely on 3D shapes, it provides an estimate of the success of the calibration before time-consuming processes, i.e., 3D scanning and 2D-3D correspondence mapping.