A regularization-based approach to 3-D reconstruction from multiple images is proposed. As one of the most widely used multiple-view 3-D reconstruction algorithms, Space Carving can produce a photo hull of a scene, which is at best a coarse volumetric model. The two-view stereo algorithm, on the other hand, can generate a more accurate reconstruction of the surfaces, provided that a given surface is visible to both views. The proposed method is essentially a data fusion approach to 3-D reconstruction, combining the above two algorithms by means of regularization. The process is divided into two steps: (1) computing the photo hull from multiple calibrated images, and (2) selecting two of the images as input and solving the two-view stereo problem by global optimization, using the photo hull as the regularizer. The dynamic programming implementation of this regularization-based stereo approach potentially provides an efficient and robust way of reconstructing 3D surfaces. The result of an implementation of this theory is presented on a real data set and compared with peer algorithms.