Mixed reality is different from the virtual reality in that users can feel immersed in a space which is composed of not only virtual but also real objects. Thus, it is essential to realize seamless integration and interaction of the virtual and real worlds. We need depth information of the real scene to synthesize the real and virtual objects. We propose a two-stage algorithm to find smooth and precise disparity vector fields with sharp object boundaries in a stereo image pair for depth estimation. Hierarchical region-dividing disparity estimation increases the efficiency and the reliability of the estimation process, and a shape-adaptive window provides high reliability of the fields around the object boundary region. At the second stage, the vector fields are regularized with a energy model which produces smooth fields while preserving their discontinuities resulting from the object boundaries. The vector fields are used to reconstruct 3D surface of the real scene. Simulation results show that the proposed algorithm provides accurate and spatially correlated disparity vector fields in various kinds of images, and synthesized 3D models produce natural space where the virtual objects interact with the real world as if they are in the same world.