Inferring bias and uncertainty in camera calibration

• URL: http://arxiv.org/abs/2107.13484v1
• Date: Wed, 28 Jul 2021 16:49:39 GMT
• Title: Inferring bias and uncertainty in camera calibration
• Authors: Annika Hagemann, Moritz Knorr, Holger Janssen, Christoph Stiller
• Abstract summary: We introduce an evaluation scheme to capture the fundamental error sources in camera calibration. The bias detection method uncovers smallest systematic errors and reveals imperfections of the calibration setup. A novel re-sampling-based uncertainty estimator enables uncertainty estimation under non-ideal conditions. We derive a simple uncertainty metric that is independent of the camera model.
• Score: 2.11622808613962
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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