非刚性变换的三维等距模型的对应关系研究  

Research on Shape Correspondence of 3D Isometric Models Differing by Non-Rigid Deformations

作　　者：杨军[1] 李龙杰[1] 田振华[2] 王小鹏[1] 

机构地区：[1]兰州交通大学电子与信息工程学院,兰州730070 [2]兰州交通大学自动化与电气工程学院,兰州730070

出　　处：《计算机科学与探索》2014年第8期1009-1016,共8页Journal of Frontiers of Computer Science and Technology

基　　金：国家自然科学基金 No.61261029;中国博士后科学基金 No.2013M542396;人社部留学人员科技活动项目择优资助;甘肃省自然科学基金 No.1208RJZA243;陇原青年创新人才扶持计划 No.201182~~

摘　　要：针对非刚性变换后两个三维等距模型间的对应关系问题,提出了基于极点谱植入初始化的贪婪优化算法。首先运用基于高斯曲率的最远点采样算法,获得一组数目相同和位置相对一致的采样点;其次改进初始谱植入匹配算法建立两模型采样点集间的初始对应关系;最后使用基于全局度量(测地距离)的贪婪优化算法进行迭代优化,从而得到三维模型间的稀疏对应关系。实验结果表明,改进的非刚性匹配算法能够获得强健的稀疏对应关系,并在一定程度上提高了匹配算法的效率。This paper proposes a greedy optimal algorithm based on the initialization of spectral embedding of extreme points in order to calculate optimal correspondence between two given 3D isometric shapes after non-rigid transformation. Firstly, a group of sample points with same quantity and relatively consistent position are obtained by using FPS （farthest point sampling） algorithm based on Gaussian curvature. Then, an improved matching algorithm of spectral embedding is adopted to establish initial correspondence between the sampling point sets. Finally, sparse correspondence between isometric shapes is iteratively computed by a greedy optimal algorithm based on global metrics （geodesic distance）. According to experimental results, the proposed algorithm can get robust sparse correspondence and improve the efficiency of the matching algorithm in a certain extent.

关 键 词：非刚性变换 极点 全局度量 稀疏对应关系 贪婪优化算法 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]