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.

准确的相机校准是许多计算机视觉应用的先决条件。校准错误，例如错误的模型假设或不精确的参数估计，会降低系统的整体性能，因此对这些错误的可靠检测和量化至关重要。在这项工作中，我们引入了一种评估方案来捕获相机校准中的基本误差源：系统误差（偏差）和不确定性（方差）。所提出的偏差检测方法揭示了最小的系统误差，从而揭示了校准设置的缺陷，并为相机模型选择提供了基础。一种新的基于重采样的不确定性估计器可以在非理想条件下进行不确定性估计，从而扩展了经典协方差估计器。此外，我们推导出一个简单的不确定性度量，它独立于相机模型。结合起来，所提出的方法可用于评估单个校准的准确性，也可用于对新校准算法、相机模型或校准设置进行基准测试。我们通过模拟和真实相机评估所提出的方法。