基于模糊逻辑的三维散乱数据非均匀简化算法

Non-uniform Simplification Algorithm of 3D Unorganized Point Data Based on Fuzzy Logic Concept

研究了三维散乱数据的非均匀简化问题,给出了法向变化量的定义及其与曲面高斯曲率的关系。在此基础上,提出了一种新的基于模糊逻辑的非均匀简化算法。该算法可通过初始分块立方体的大小调节平坦区域数据点的密度,也可通过对不同的模糊集设置不同的细分函数,调节中、大曲率区域数据点的密度。算法还采用了分别在每个含点立方体内进行法向一致性调整的方法,提高了法向调整的速度。应用实例表明了算法的实用性和有效性。

Based on the study of relationship between local normal change of sample points and the surface Gauss curvature, a new nonuniform simplification algorithm using fuzzy logic concept was proposed. The algorithm can adjust the density of points over flat or smooth areas by the bin size and the density over rough areas by subdividing function respectively. The algorithm also can consistently orientate normal vectors in each cube containing the points, thus greatly improving the orientation-consisting speed. Experimental results show that the algorithm is effective and feasible.