This paper describes an algorithm for 3D reconstruction of a smooth surface with a relatively dense set of self-similar point features from two calibrated views. We bypass the usual correspondence problem by triangulating a point in space from all pairs of features satisfying the epipolar constraint. The surface is then extracted from the resulting point cloud by taking advantage of the statistical and geometric properties of the point distribution on the surface. Results are presented for computer simulations and for a laboratory experiment on a silicon gel phantom used in a breast cancer screening project.