A novel approach to a measuring algorithm and calibration method for 3-D coordinate noncontact measurement has been developed. This algorithm introduces variables that relate the output digital image directly to the viewed 3-D object. A simple and efficient calibration procedure is achieved using the least-squares technique. It takes advantage of a 2-D coordinate mapping relation, along with dual CCD cameras and a 3-D calibration grid consisting of a series of characteristic points with known coordinates in real space. The space coordinates of these characteristic points are compared to their image coordinates in the CCDs. A 3-D mapping function between the actual space and its image planes in the dual CCD cameras is then constructed. The relationship between the two is determined accordingly and can be applied to extract 3-D information from a tested object sitting in a specific space with satisfactory accuracy. This algorithm not only significantly reduces the 3-D measurement computations, but also increases the measurement precision and speed. Current experimental results show that the measurement standard deviation is under 0.06 mm in a bounded space of 80 x60x60 mm. This result reveals that this measurement algorithm has the potential to be an effective positioning method for rapid and precise noncontact coordinate measuring systems.