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A challenge in image based metrology and forensics is intrinsic cameracalibration when the used camera is unavailable. The unavailability raises twoquestions. The first question is how to find the projection model thatdescribes the camera, and the second is to detect incorrect models. In thiswork, we use off-the-shelf extended PnP-methods to find the model from 2D-3Dcorrespondences, and propose a method for model validation. The most commonstrategy for evaluating a projection model is comparing different models'residual variances - however, this naive strategy cannot distinguish whetherthe projection model is potentially underfitted or overfitted. To this end, wemodel the residual errors for each correspondence, individually scale allresiduals using a predicted variance and test if the new residuals are drawnfrom a standard normal distribution. We demonstrate the effectiveness of ourproposed validation in experiments on synthetic data, simulating 2D detectionand Lidar measurements. Additionally, we provide experiments using data from anactual scene and compare non-camera access and camera access calibrations.Last, we use our method to validate annotations in MegaDepth.

Camera Calibration without Camera Access -- A Robust Validation Technique for Extended PnP Methods