GAO Xue-yao, DONG Xin-ran, ZHANG Chun-xiang. Model Similarity Calculation Based on Adaptive Global Optimal Harmony Search[J]. 2020, 25(6): 150-156.DOI: 10.15938/j.jhust.2020.06.022.
摘 要: 为了度量模型之间的差异,提出了一种基于自适应全局最优和声搜索算法( self-adap-tive global best harmony search algorithm,SGHS) 的三维模型相似性计算方法。根据面的组成边数和 面的邻接关系来构造 2 个模型之间的面相似度矩阵。从面相似度矩阵中,利用自适应全局最优和 声搜索算法获得 2 个模型之间的最优面匹配序列。根据最优面匹配序列,累积源模型面与目标模 型面之间的相似度来计算模型之间的相似性。实验结果表明: 所提出方法更能准确地度量模型之 间的差异。
Abstract
In order to measure the difference of models,a method of computing three dimension model’ssimilarity based on self-adaptive global best harmony search algorithm is proposed. Two models’face similarity matrix is constructed according to the number of face’s edges and adjacency relationship of faces. Self-adaptive global best harmony search algorithm is used to find an optimal face matching sequence between source model and target one from face similarity matrix. Based on an optimal face matching sequence,similarities between source model faces and target ones are summed to compute two models’similarity. Experimental results show that the proposed method can measure two models’difference more accurately.