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

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.

Key words: non-rigid transformation, extreme point, global metrics, sparse correspondence, greedy optimal algorithm