Creating Realistic Ground Truth Data for the Evaluation of Calibration Methods for Plenoptic and Conventional Cameras
This addresses a critical need for unbiased evaluation in camera calibration, particularly for plenoptic cameras where no synthetic data existed, though it is incremental as it builds on existing calibration frameworks.
The paper tackles the problem of biased evaluation in camera calibration due to unrealistic synthetic data, proposing a backward ray tracing method to create realistic ground truth data for both conventional and plenoptic cameras, enabling unbiased assessment.
Camera calibration methods usually consist of capturing images of known calibration patterns and using the detected correspondences to optimize the parameters of the assumed camera model. A meaningful evaluation of these methods relies on the availability of realistic synthetic data. In previous works concerned with conventional cameras the synthetic data was mainly created by rendering perfect images with a pinhole camera and subsequently adding distortions and aberrations to the renderings and correspondences according to the assumed camera model. This method can bias the evaluation since not every camera perfectly complies with an assumed model. Furthermore, in the field of plenoptic camera calibration there is no synthetic ground truth data available at all. We address these problems by proposing a method based on backward ray tracing to create realistic ground truth data that can be used for an unbiased evaluation of calibration methods for both types of cameras.