Low-level precision boundary information of the unknown surface is useful for digital sensor path planning, data segmentation and feature recognition in reverse engineering. In order to acquire the low-level boundary information of the measured surface both rapidly and easily, the Coaxial Stereo Vision Photogrammetry (CSVP) was systematically studied. The mathematical model of CSVP was established, the influences of system parameters including the focal length of the cameras, the baseline distance and the spatial position of the measured points on measuring accuracy were analyzed, and the mathematical analysis for determining the optimum range of the baseline distance of the two cameras was carried out. In addition, the characteristics of the CSVP epipolar geometry were studied. Then a new approach for rapid digitization of surface boundaries based on CSVP was proposed. This approach was characterized by an integrated use of a Coordinate Measuring Machine (CMM) and a single CCD camera, the CMM was employed to provide an accurate and repeatable platform for the camera. Consequently, the camera can be located at any predefined position within the CMM’s working volume. This enables the camera to get stereo image pairs in different positions along the same axis, which can only be achieved by two cameras without the CMM. During data processing, the characteristics of the CSVP epipolar geometry, in which the corresponding epipolar lines in the front and back image planes are arranged parallel to each other, and the epipolar lines pass through the main points of their own image planes respectively, were applied to facilitate stereo matching, by which the surface boundary information of the measured surface can be obtained both rapidly and easily compare to conventional binocular stereo vision method. Experimental results indicate that the average error of the surface boundaries reconstructed by the data gathered by the proposed method is 0.268 mm. It can meet the accuracy requirements of low-level boundary information in reverse engineering.