A feature-preserving simplification based on integral invariant clustering

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 mode...

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Bibliographic Details
Published in2009 11th IEEE International Conference on Computer-Aided Design and Computer Graphics pp. 210 - 216
Main Authors Zhe Bian, Peng Zhao
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.08.2009
Subjects
Application software
Clustering algorithms
Computer architecture
Computer graphics
Computer science
Computer vision
Information science
Laboratories
Manufacturing industries
Virtual manufacturing
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Summary: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 find 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 feature-preserving, and it can also be combined with other mesh simplification schemes to improve their effects.
ISBN:9781424436996
1424436990
DOI:10.1109/CADCG.2009.5246903