Inferring bias and uncertainty in camera calibration
This work addresses the critical need for reliable error assessment in camera calibration, which is a precondition for many computer vision applications, but it appears incremental as it builds on existing calibration frameworks.
The paper tackles the problem of detecting and quantifying systematic errors and uncertainty in camera calibration, introducing methods that reveal imperfections in calibration setups and enable uncertainty estimation under non-ideal conditions, with evaluation conducted through simulations and real cameras.
Accurate camera calibration is a precondition for many computer vision applications. Calibration errors, such as wrong model assumptions or imprecise parameter estimation, can deteriorate a system's overall performance, making the reliable detection and quantification of these errors critical. In this work, we introduce an evaluation scheme to capture the fundamental error sources in camera calibration: systematic errors (biases) and uncertainty (variance). The proposed bias detection method uncovers smallest systematic errors and thereby reveals imperfections of the calibration setup and provides the basis for camera model selection. A novel resampling-based uncertainty estimator enables uncertainty estimation under non-ideal conditions and thereby extends the classical covariance estimator. Furthermore, we derive a simple uncertainty metric that is independent of the camera model. In combination, the proposed methods can be used to assess the accuracy of individual calibrations, but also to benchmark new calibration algorithms, camera models, or calibration setups. We evaluate the proposed methods with simulations and real cameras.