基于逆向参数化的B样条曲面重建算法

目的解决B样条曲面重建问题中矩形拓扑网自动生成和参数化两大难点问题,提出一种基于逆向参数化的B样条曲面重建算法.方法首先构建基曲面,在基曲面上根据参数(u,v)进行采样,沿其法线方向进行数据的滤波和精简,求得参数(u,v)对应的精简点,然后对采样求取的精简点集进行B样条曲面拟合,该方法提供了B样条曲面重建的一个新思路.结果新算法突破了传统密集散乱点云数据的B样条曲面重建基本过程,采用与正向参数化相反的过程进行参数化,解决了B样条曲面重建问题中矩形拓扑网自动生成和参数化的难题;具体试验分析表明新算法不仅在参数化的同时完成了数据滤波和精简,而且在时间和迭代效率方面都具有优势.结论新算法避免了求取法线的迭代过程,并且可以较容易的实现矩形拓扑网的自动生成,新算法在自主开发的智能测量建模加工一体化装备中得到了应用验证.

In order to solve the difficulties of parameterization and automatic rectangular network construction,a new inverse parameterization method based on B-spline surface reconstruction is proposed.Unlike the traditional process,the new method adopts an inverse process: constructing a base surface,base surface resampling,getting the corresponding refined point to the sample point along its normal,and then B-spline surface fitting.This provides a new idea to solve the B-spline surface reconstruction problem.This new algorithm is quite different from traditional method,which solved the difficulties of parameterization and automatic rectangular network construction.Experiments analysis shows that the new method can not only finish the data filtering and refinement during parameterization,but also shows great advantages in time and iteration efficiency.The new algorithm avoids the iteration process of normal computation,and realizes the automatic construction of rectangular network more easily.It is already proved by real application in our independent developed intelligent devices of measuring,modeling and manufacturing.

863计划引导项目资助(2004AA001090)