Focusing on the ambiguity in stereo matching

which induces the absence of robustness on occlusion and illumination condition

a novel multi-view reconstruction method is proposed based on a two-step strategy. The stereo matching problem is converted into an issue of color variance minimization using the second fundamental property of visual hull

and a threshold is employed to balance the contradiction between the sampling number and sampling noise. A cost functional is given by combining the position potential energy and the elasticity potential energy of the moving surface. The cost functional is minimized using second-order center difference and upwind design for Hamilton-Jacobi equation so that the object surface is approximated by level set functions

and the sampling noise is filtered. The surface of the object is then reconstructed completely. Not only the inherent ambiguity in stereo matching is avoided but also the robustness on occlusion and illumination condition is strengthened. The experimental results show that the shape of an object can be concluded from real complex images with high accuracy and robustness

and that the reconstructed time is saved about 26% to 33%

compared with the classical Hoppe's algorithm.