New Algorithm for Multi-symmetry Models in 3D Shape Matching

Three-dimensional shape matching has a wide range of applications in the field of computer vision, among which symmetric shape matching has always been one of the difficult problems because its geometric features are very similar and difficult to distinguish. This thesis is focused on the matching problem of 3D multi-symmetric shape. In this thesis, a new algorithm of multi-symmetric shape matching is proposed with a multi-arthropod model as the research object. The main steps are: First, select the feature points, that is, based on the extreme value point of the Heat Kernel Signature, the farthest point sampling and fusion algorithm is used to adjust the number of feature points to obtain the feature point set; Second, classify the feature points, the concept of symmetry difference and supporting point pairs is introduced to divide feature points into symmetric points and asymmetric points, and then geodesic distance is used to further divide the symmetric points to improve the matching accuracy in the later stage; Third, conduct the shape matching, the algorithm is given to determine a reference point in the set of asymmetric points, using the distance ordering of the symmetry point from the reference point to complete the initial matching; In order to adjust the possible left-right cross error problem, the normal of shape is determined, and by judging whether the cross product direction of the vector formed by the symmetry point and the reference point is consistent with the normal to obtain the correct matching result. The experimental results of this algorithm in TOSCA database show that the correctness and operation efficiency of this algorithm are improved compared with the existing algorithms for the ant and spider models, both in symmetric point detection and in matching results.