3D reconstruction approach based on neural network

In this paper, a new 3D reconstruction approach in neuro-vision system is presented. Firstly, RBF network (RBFN) is used to provide effective methodologies for solving camera calibration and stereo rectification problems. RBFN works mainly in two aspects: (1) a RBFN is adopted to learn and memorize the nonlinear relationship in stereovision system; (2) another RBFN is trained to search the correspondent lines in two images such that stereo matching could be performed in one dimension. Secondly, a new matching method based on Hopfield neural network (HNN) is presented. The energy function is built on the basis of uniqueness, compatibility and similarity constraints. It is then mapped onto a 2-D neural network for minimization, whose final stable state indicates the possible correspondence of the matching units. The depth map can be acquired through performing the above operation on the all epipolar lines. Experiments have been performed on common stereo pairs and the results are accurate and convincing.