Camera calibration using synthetic random speckle pattern and digital image correlation

Accurate camera calibration is of fundamental importance to various vision-based 3D metrological techniques. Despite camera calibration methods using regular planar calibration targets (e.g., checkerboard or circular pattern) have been widely adopted, their accuracy is less-than-desirable due to the limited number and the low registration accuracy of control points. This work presents an alternative calibration method that uses a calibration target based on a synthetic random speckle pattern. Specifically, a set of regularly distributed control points is first specified on the synthetic speckle pattern displayed on a monitor. These control points are then precisely matched to their counterparts on the captured calibration images using the state-of-the-art digital image correlation (DIC) algorithm. Compared with the existing camera calibration methods, the proposed method possesses the advantages of much more effective control points, higher control point match accuracy, which lead to more accurate and precise estimation of camera parameters. To evaluate the performance of the proposed calibration method, simulated calibration tests using images with varied noise levels and real calibration tests were performed on the planar calibration targets using speckle, circular and checkerboard patterns. The influences of various DIC calculation parameters (i.e. the number of control points and the subset size) on calibration results are also studied using noiseless simulated images. The reprojection errors on synthetic noiseless images and real images are computed as 0.004 and 0.038 pixels, respectively, confirming the high calibration accuracy delivered by the proposed method.