B-Spline Surface Fitting on Scattered Points

This paper looks into the effectiveness of B-spline approxi mat on algorithm in approximating the bicubic B-spline sur face from the set of scattered data points which are taken from the scanned 3D object in the form of point sets. Using the B-splin e approximation algorithm, the unknown B-spline control poi nts are determined, followed by the reconstruction of the bi cu ic B-spline surface. Using a set of neighbourhood of data points, a B-spl ine surface patch may be constructed, which can be pieced tog ther to form the final surface. Modification of the B-spline approxim ation algorithm is carried out before the reconstruction in order to fit the scattered data points closely. Here, the density of the data points is scaled down due to the sparseness of the points that may affect the smoothness. The sample of scattered data points is chosen fr om a specific region in the point set model by using k-nearest neighbour search method. Furthermore, to fit the sample set of scattere d data points accurately, they are reoriented in the normal d irection. We also observe the effect of noise in the reconstruction of bic ubic B-spline surface. Experimental results demonstrate t hat the scattered data points are better fitted after the modification of the alg orithm and the accuracy of the approximated bicubic B-splin e surface is easily influenced by the presence of noise.