Surface Reconstruction Method Based on GRNN

A surface reconstruction method based on generalized regression neural net (GRNN) is presented. First, in order to eliminate noise points, some sample points are chosen from the measured data to construct GRNN. Thus a neural net to approximate the measured points is obtained. And the distribution probability of the approximation error is figured out. In result, the noise points are eliminated when their error probability is less than the threshold value. Then the boundary points are extracted. Lastly the surface model is reconstructed by use of the measured points from which noise points have been eliminated. The reconstruction error is analyzed. The results indicate that the reconstruction precision can satisfy the demands of engineering application.