Research on the reconstruction method of B-spline surface based on radius basis function neural networks

Surface reconstruction is the key technology in the geometry reverse engineering. In order to obtain the object's geometrical model, we have to construct surface by large numbers of measured data points. This paper introduces a new method for the reconstruction of free-form surface. Firstly, it profits scattered and measured data points which come from free-form surface archetype by radius basis function neural networks algorithm. Secondly, it maps the mathematical model of free-form surface by the linear combination of radius basis function and the weights of the hidden layer. Finally it transforms the mathematical model to bicubic B-spline surface. This paper also commentates the feasibility of the above idea that resolves the problems of surface fitting by radius basis function neural networks