标题:Extraction of Feature Points on 3D Meshes Through Data Gravitation
作者:Wang, Chengwei; Kang, Dan; Zhao, Xiuyang; Peng, Lizhi; Zhang, Caiming
通讯作者:Zhao, Xiuyang
作者机构:[Wang, Chengwei; Kang, Dan; Zhao, Xiuyang; Peng, Lizhi] Univ Jinan, Sch Informat Sci & Engn, Jinan 250022, Peoples R China.; [Wang, Chengwei; Kang, 更多
会议名称:12th International Conference on Intelligent Computing (ICIC)
会议日期:AUG 02-05, 2016
来源:INTELLIGENT COMPUTING THEORIES AND APPLICATION, ICIC 2016, PT II
出版年:2016
卷:9772
页码:601-612
DOI:10.1007/978-3-319-42294-7_54
关键词:Data gravitation; Feature points; 3D mesh; Gaussian curvature
摘要:Feature points are particularly simple elements which demonstrate a model efficiently and availably; nonetheless, the points on 3D models cannot be extracted completely yet. Therefore, we propose a new algorithm based on data gravitation to extract the feature points on 3D meshes. First, we select the point with the maximum Gaussian curvature as the initial feature point set. Then, we use farthest point sampling to calculate the farthest distance from feature point set, and add this point into feature point set. Next we use the farthest distance to calculate data gravitation and select the point with the largest data gravitation until the farthest distance is smaller than a given threshold. Finally we get the feature points set on 3D meshes. In our experiments, we compare our algorithm with other algorithms. Results show that our algorithm can capture feature points effectively; consequently, the set of feature points reflects the features of 3D meshes precisely. Moreover, our algorithm is simple and is therefore easy to implement.
收录类别:CPCI-S;EI;SCOPUS
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-84978828141&doi=10.1007%2f978-3-319-42294-7_54&partnerID=40&md5=81bee9a51398edf1e977414649888087
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