There is no such universally applicable theory for features extraction, leading to a great diversity of stereo correspondence in Binocular Stereo Vision research. Stereo correspondence is to establish the correspondence between primitive factors in images, i.e. to match P1(u1,v1) and P2(u2,v2) from two images. Certain interference factors in the scenario should be noticed, e.g. illumination, noise, surface physical characteristic and etc. According to precise correspondence, combined with camera location parameters, 3D geometric information can be recovered without difficulties. Due to the fact that accuracy of 3D reconstruction depends on the precision of correspondence, error of camera location parameters and so on, the previous procedures must be done carefully to achieve relatively accurate 3D reconstruction. Clinical routine of diagnosis, patient follow-up, computer assisted surgery, surgical planning etc. are facilitated by accurate 3D models of the desired part of human anatomy. Main motivation behind 3D reconstruction includes Applications:  3D reconstruction has applications in many fields. They are:  Problem Statement:  Mostly algorithms available for 3D reconstruction are extremely slow and cannot be used in real-time. Though the algorithms presented are still in infancy but they have the potential for fast computation. Existing Approaches:  Delaunay and alpha-shapes Both methods have been recently extended for reconstructing point clouds with noise. In this method the quality of points determines the feasibility of the method. For precise triangulation since we are using the whole point cloud set, the points on the surface with the error above the threshold will be explicitly represented on reconstructed geometry. Zero set Methods  Reconstruction of the surface is performed using a distance function which assigns to each point in the space a signed distance to the surface "S". A contour algorithm is used to extracting a zero-set which is used to obtain polygonal representation of the object. Thus, the problem of reconstructing a surface from a disorganized point cloud is reduced to the definition of the appropriate function "f" with a zero value for the sampled points and different to zero value for the rest. An algorithm called marching cubes established the use of such methods. There are different variants for given algorithm, some use a discrete function "f", while other use a polyharmonic radial basis function is used to adjust the initial point set. Functions like Moving Least Squares, basic functions with local support, based on the Poisson equation have also been used. Loss of the geometry precision in areas with extreme curvature, i.e., corners, edges is one of the main issues encountered.