The physical world around us is three-dimensional (3D); yet traditional cameras and imaging sensors are able to acquire only two-dimensional (2D) images that lack the depth information. This fundamental restriction greatly limits our ability to perceive and to understand the complexity of real-world objects. The past several decades have marked tremendous advances in research, development, and commercialization of 3D surface imaging technologies, stimulated by application demands in a variety of market segments, advances in high-resolution and high-speed electronic imaging sensors, and ever-increasing computational power. The term “3D imaging” refers to techniques that are able to acquire true 3D data, i.e., values of some property of a 3D object, such as the distribution of density, as a function the 3D coordinates (x, y, z). A more general 3D surface imaging system is able to acquire a scalar value, such as surface reflectance, associated with each point on the nonplanar surface. One principal method of 3D surface imaging is based on the use of “structured light,” i.e., active illumination of the scene with specially designed 2D spatially varying intensity pattern. An imaging sensor (a video camera, for example) is used to acquire a 2D image of the scene under the structured-light illumination. If the scene is a planar surface without any 3D surface variation, the pattern shown in the acquired image is similar to that of the projected structured-light pattern. However, when the surface in the scene is nonplanar, the geometric shape of the surface distorts the projected structured-light pattern as seen from the camera. The principle of structured-light 3D surface imaging techniques is to extract the 3D surface shape based on the information from the distortion of the projected structured-light pattern. Accurate 3D surface profiles of objects in the scene can be computed by using various structured-light principles and algorithms.

 The 3D modeling of object surface is one of the most important tasks of close-range photogrammetry when considering expanding range of different applications, considering focusing by a wide range of users and researchers. Numerous techniques for surface imaging by structured light are
currently available.  The various methods of 3D modeling depend on different factors such as the cost, distance and dispersion of measurement points, the amount of surface object deformation, characteristics of the work environment, time allowed for the measurement, object type, texture and color of the object, the stationary or moving of objects. In addition, in all these methods, automate of reconstruction and the automatic matching of corresponding points are the main challenges. Among these methods, the structured light method can be considered as a method to help automate of 3D reconstruction, when does not exist possibility of installation on the target object or is not possible to determine the high-density and automatic corresponding points (due to the same texture object). In this method, to produce 3D data of the object, a light pattern with known geometrical structure are projected on the surface of the object by means of projection tool. Then, the depth of the object is calculated using the distortion of the image taken by the camera. In the structured light method, as well as other methods, the main problem is determining corresponding points automatically, which is used to solve the coded light patterns. The aim of the research is design, construction and evaluation of modeling 3D objects that have the same texture and without the need to target investment, using structured light method. To this end, the Power shot G3 digital camera and Infocus X2 projector are used to take images and to project of structured light on the surface of the object, respectively. In this research, to achieve optimal accuracy and performance of 3D reconstruction process automatically, and create a unique identifier for automatic matching of each point measurement has been used 14 binary light pattern. To implement of 3D modeling, a statue 120´50 cm dimensions were chosen with the same texture. Then, to increase the density of 3D points extracted from the surface, in addition to binary coding, the phase transition was used. The results showed that the proposed method could be implemented with an average of ±100 micron accuracy in terms of the root mean square error (RMSE), to extract 3D information of surface objects.