Camera Calibration without Camera Access -- A Robust Validation Technique for Extended PnP Methods
This work addresses a challenge in image-based metrology and forensics for scenarios where camera access is limited, though it is incremental as it builds on existing extended PnP methods.
The paper tackles the problem of validating camera projection models when the camera is unavailable, using a method that scales residuals and tests for normality to detect underfitting or overfitting, achieving effective validation on synthetic and real data including MegaDepth annotations.
A challenge in image based metrology and forensics is intrinsic camera calibration when the used camera is unavailable. The unavailability raises two questions. The first question is how to find the projection model that describes the camera, and the second is to detect incorrect models. In this work, we use off-the-shelf extended PnP-methods to find the model from 2D-3D correspondences, and propose a method for model validation. The most common strategy for evaluating a projection model is comparing different models' residual variances - however, this naive strategy cannot distinguish whether the projection model is potentially underfitted or overfitted. To this end, we model the residual errors for each correspondence, individually scale all residuals using a predicted variance and test if the new residuals are drawn from a standard normal distribution. We demonstrate the effectiveness of our proposed validation in experiments on synthetic data, simulating 2D detection and Lidar measurements. Additionally, we provide experiments using data from an actual scene and compare non-camera access and camera access calibrations. Last, we use our method to validate annotations in MegaDepth.