One-dimensional dense disparity estimation for three-dimensional reconstruction

We present a method for fully automatic three-dimensional (3-D) reconstruction from a pair of weakly calibrated images in order to deal with the modeling of complex rigid scenes. A two-dimensional (2-D) triangular mesh model of the scene is calculated using a two-step algorithm mixing sparse matching and dense motion estimation approaches. The 2-D mesh is iteratively refined to fit any arbitrary 3-D surface. At convergence, each triangular patch corresponds to the projection of a 3-D plane. The algorithm proposed here relies first on a dense disparity field. The dense field estimation modelized within a robust framework is constrained by the epipolar geometry. The resulting field is then segmented according to homographic models using iterative Delaunay triangulation. In association with a weak calibration and camera motion estimation algorithm, this 2-D planar model is used to obtain a VRML-compatible 3-D model of the scene.