Automatic 3-D model synthesis from measured range data

We propose an algorithm to construct a 3-D surface model from a set of range data, based on non-uniform rational B-splines (NURBS) surface-fitting technique. It is assumed that the range data is initially unorganized and scattered 3-D points, while their connectivity is also unknown. The proposed algorithm consists of three stages: initial model approximation employing K-means clustering, hierarchical decomposition of the initial model, and construction of the NURBS surface patch network. The initial model is approximated by both a polyhedral and triangular model. Then, the initial model is represented by a hierarchical graph, which is efficiently used to construct the G ¹ continuous NURBS patch network of the whole object. Experiments are carried out on synthetic and real range data to evaluate the performance of the proposed algorithm. It is shown that the initial model as well as the NURBS patch network are constructed automatically with tolerable computation. The modeling error of the NURBS model is reduced to 10%, compared with the initial mesh model