| 非刚性变换的三维等距模型的对应关系研究 |
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| 引用本文： | 杨军,;李龙杰,;田振华,;王小鹏.非刚性变换的三维等距模型的对应关系研究[J].计算机科学与探索,2014(8):1009-1016. |
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| 作者姓名： | 杨军 ;李龙杰 ;田振华 ;王小鹏 |
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| 作者单位： | [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 |
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| 基金项目： | The National Natural Science Foundation of China under Grant No. 61261029 （国家自然科学基金）; the Postdoctoral Science Foundation of China under Grant No. 2013M542396 （中国博士后科学基金）; the Technology Foundation for Selected Overseas Chinese Scholar of Ministry of Human Resources and Social Security of China （人社部留学人员科技活动项目择优资助）; the Natural Science Foun- dation of Gansu Province of China under Grant No. 1208RJZA243 （甘肃省自然科学基金）; the Longyuan Support Program for Young Innovative Talents under Grant No. 201182 （陇原青年创新人才扶持计划）. |
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| 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。
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| 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 |
Research on Shape Correspondence of 3D Isometric Models Differing by Non-Rigid Deformations |
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| Affiliation: | YANG Jun, LI Longjie, TIAN Zhenhua, WANG Xiaopeng( 1. School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China ;2. School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China) |
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| Abstract: | 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. |
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| Keywords: | non-rigid transformation extreme point global metrics sparse correspondence greedy optimal algorithm |
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