Simple calibration method for dual-camera structured light system

Abstract

A dual-camera structured light system consisting of two cameras and a projector has been widely researched in three-dimensional (3D) profilometry. A vital step in these systems is 3D calibration. Existing calibration methods are time-consuming and complicated because each camera-projector pair is calibrated separately. In this paper, an improved calibration method is proposed to decrease the calibration effort by simplifying the extrinsic calibration of one camera-projector pair. It needs only two texture images to acquire the extrinsic parameters of the right camera-projector pair instead of 25 images (a texture image, 12 vertical, and 12 horizontal sinusoidal fringe patterns) and more complicated computations. A variant iterative closest point (ICP) algorithm was studied to match 3D cloud data sets for each camera-projector, and to reject outliers and invisible data automatically at each iterative step by using the proposed five criteria. Experimental results demonstrate that the proposed method is simple to operate and reaches the higher measurement accuracy of the shape data compared with the existing state of the art method.