Abstract: A method for assessing a camera calibration. In the method, a first quality measure is ascertained, a systematic error having to be assessed using the first quality measure, the assessment being carried out with respect to errors remaining after a calibration, an overall calibration object including at least one calibration object being virtually segmented into calibration objects, a detector noise being estimated for each calibration object, which are combined to form an overall estimate, which is compared with an estimate of the detector noise of the overall calibration object.

Abstract: A computer-implemented method for self-calibration of at least one camera. The method comprises: ascertaining at least two corresponding images from a sequence of recorded images, the recorded images resulting from recordings of the camera, the corresponding images having correspondences which are specific to at least one camera parameter, the camera parameter being specific to the geometric imaging behavior of the camera; ascertaining the respective correspondence by an application of an artificial neural network, the application being based on the corresponding images; determining at least the camera parameter based on the ascertained correspondences for the self-calibration of the camera.

Abstract: A method for assessing a camera calibration, in which a first quality measure is ascertained. A statistical error is assessed using the first quality measure. An expected value for a mapping error is ascertained. Optimal model parameters and their covariance matrix initially are accessed. A matrix of a mapping error is then determined. Finally, the expected value of the mapping error is ascertained.

Abstract: A method for assessing a camera calibration. In the method, a first quality measure is ascertained, a systematic error having to be assessed using the first quality measure, the assessment being carried out with respect to errors remaining after a calibration, an overall calibration object including at least one calibration object being virtually segmented into calibration objects, a detector noise being estimated for each calibration object, which are combined to form an overall estimate, which is compared with an estimate of the detector noise of the overall calibration object.

Abstract: A method for assessing a camera calibration, in which a first quality measure is ascertained. A statistical error is assessed using the first quality measure. An expected value for a mapping error is ascertained. Optimal model parameters and their covariance matrix initially are accessed. A matrix of a mapping error is then determined. Finally, the expected value of the mapping error is ascertained.