We propose an automatic calibration method using grid-structured light for the full parameter of a camera-projector system, including principal points, equivalent focal length, image distortion coefficients, the rotation matrix, and translation vectors between the camera and projector. Grid-structured light is projected onto a board, camera image intersection points are extracted, and three-dimensional intersection points are computed according to a homography matrix. Finally, the full parameter of a camera-projector system is solved based on stereo vision. No manual intervention is required during image processing, which simplifies the operations and improves efficiency. The image-processing kernel problem involves both automatic detection and intersection point matching. (1) The proposed intersection point detection method utilizes multiscale fusion. At each level of the image pyramid, intersection points are searched according to gray distribution and geometrical characteristics. With the gray-gravity method, coordinates are achieved with subpixel intersection point precision. Therefore, the location precision exceeds 0.5 pixel. (2) The proposed matching method employs belief propagation. Taking intersection points as nodes, a Bayesian network is established according to the Markov random field hypothesis. The image intersection point matching problem between a camera and the projector is then transformed into a maximum a posteriori estimation problem. Ultimately, 15 images are used to calibrate the full parameter of a camera-projector system. The results indicate that the reprojection error exceeds 0.15 pixel.
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