Simultaneous Projector-Camera Self-Calibration for Three-Dimensional Reconstruction and Projection Mapping

Automatic calibration of structured-light systems, generally consisting of a projector and camera, is of great importance for a variety of practical applications. We propose a novel optimization approach for geometric calibration of a projector-camera system that estimates the intrinsic, extrinsic, and distortion parameters of both the camera and projector in an automatic fashion using structured light. Our approach benefits from a novel multifactor objective function that finds maximum-likelihood estimates from noisy point correspondences using constraints on focal lengths and resolves ambiguities estimating the fundamental matrix by enforcing epipolar geometry on the rectified noisy data. This new formulation allows estimation of all calibration parameters simultaneously and minimization is ensured by a greedy descent algorithm that decreases the cost function at each iteration. This provides more accurate parameter estimation, reconstruction accuracy, and robustness to noise and poor initialization compared to previous methods. Experimental results demonstrate the stability and robustness of our method, and show that the proposed solution outperforms a currently leading approach to an automatic geometric projector-camera calibration.