[1]贾 晖,张建刚.基于谱聚类三维模型集一致性分割算法[J].计算机技术与发展,2019,29(11):21-26.[doi:10. 3969 / j. issn. 1673-629X. 2019. 11. 005]
　JIA Hui,ZHANG Jian-gang.Consistency Segmentation Algorithm of 3D Model Set Based on Spectral Clustering[J].,2019,29(11):21-26.[doi:10. 3969 / j. issn. 1673-629X. 2019. 11. 005]
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基于谱聚类三维模型集一致性分割算法()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
29
期数:
2019年11期
页码:
21-26
栏目:
智能、算法、系统工程
出版日期:
2019-11-10

文章信息/Info

Title:
Consistency Segmentation Algorithm of 3D Model Set Based on Spectral Clustering
文章编号:
1673-629X(2019)11-0021-06
作者:
贾 晖1 ; 张建刚2
1. 西安邮电大学 计算机学院,陕西 西安 710121; 2. 西安热工研究院有限公司 电站信息及监控技术部,陕西 西安 710032
Author(s):
JIA Hui 1 ; ZHANG Jian-gang 2
1. School of Computer Science,Xi’an University of Posts and Telecommunications,Xi’an 710121,China; 2. Technological Department of Power Station Information and Monitoring,Xi’an Thermal Power Research Institute Co. ,Ltd. ,Xi’an 710032,China
关键词:
三维模型; 一致性分割; 形状直径函数; 谱聚类
Keywords:
3D model; consistency segmentation; SDF; spectral clustering
分类号:
TP301.6
DOI:
10. 3969 / j. issn. 1673-629X. 2019. 11. 005
摘要:
为提高三维模型集一致性分割算法的准确率,提出一种基于谱聚类和 SDF 特征的三维模型集一致性分割算法。该算法在具有相似形状特征的模型上进行。 选择对模型集上各个模型的部位相似性具有较好表现的 SDF 特征来描述部位的相似程度。 首先提取模型集中各个模型的面片 SDF 特征;其次计算模型各面片之间的形状相似性,形成相似性矩阵,并用测地距离对相似性矩阵进行稀疏化。 即只考虑在一定距离范围内的形状相似性,超过该范围即使形状相似也不属于一个划分。 最后采用谱聚类算法对模型集进行一致性分割。 将该算法与相关方法进行对比,实验结果表明,该算法能够对具有类似形状的模型集中的各个模型进行有意义的一致性分割,使得分割部位具有一定的对应关系,且面片平均划分准确率较好。 同时,对于模型面片简化具有较好的分割稳定度。
Abstract:
In order to improve the accuracy of 3D model set consistency segmentation algorithm,we propose an algorithm of 3D model set consistency segmentation based on spectral clustering and SDF,which is applied to the models with similar shape characteristics. The similarity of the parts is described by SDF feature which has a better representation of the part similarity of each models in the model set. At first,the face SDF feature of each model in the model set is extracted,and then the similarity between each face of the model is calculated to form similarity matrix which is sparse by the geodesic distance. That is to say,only the shape similarity within a certain distance range is considered,even the shape similarity beyond this range does not belong to a division. At last we utilize the spectral clustering algorithm to segment the model set consistently. Compared with relevant methods, the experiment shows that the proposed algorithm can consistently segment the models of the model set meaningfully,so that the segmentation parts have a certain correspondence,and the average segmentation accuracy is superior. At the same time,it has superb segmentation stability for the simplification of model surface.

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更新日期/Last Update: 2019-11-10