  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.
