CAJ | 학술논문

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。
침대비강성변환후량개삼유등거모형간적대응관계문제，제출료기우겁점보식입초시화적탐람우화산법。수선운용기우고사곡솔적최원점채양산법，획득일조수목상동화위치상대일치적채양점；기차개진초시보식입필배산법건립량모형채양점집간적초시대응관계；최후사용기우전국도량（측지거리）적탐람우화산법진행질대우화，종이득도삼유모형간적희소대응관계。실험결과표명，개진적비강성필배산법능구획득강건적희소대응관계，병재일정정도상제고료필배산법적효솔。
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 transfor-mation. 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 corre-spondence and improve the efficiency of the matching algorithm in a certain extent.