A feature-preserving simplification based on integral invariant clustering

摘要：

Detailed models are required in computer graphics for many applications. However, considering the processing and transporting time, it is often necessary to approximate these models. In this paper we provide an effective simplification method for mesh models, which decreases the size of complex models and keeps visual features. We employ the integral invariant to distinguish the desired features on the models with different scales, then use the k-means clustering algorithm to and the fixed feature vertex cluster in which the vertices are kept approximately identical by our best, finally provide a weighting map to guide the simplifications. The proposed algorithm by this paper provides significant improvement on feature-preserving, especially sharp featurepreserving, and it can also be combined with other mesh simplification schemes to improve their effects.

作者: Zhe Bian Peng Zhao

作者单位: Tsinghua National Laboratory for Information Science and Technology Department of Computer Science and Technology Tsinghua University, Beijing, China, 100084

会议类型: 国际会议

会议名称: 11th IEEE International Conference on Computer-Aided Design and Computer Graphics(第11届IEEE国际计算机辅助设计与图形学学术会议 IEEE CAD/GRAPHICS 2009)

会议地点: 黄山

会议语种:英文

页码: 210-216

在线出版日期: 2009-08-19（万方平台首次上网日期，不代表论文的发表时间）