Paper Info
Title
Laplacian-Based feature preserving mesh simplification
Abstract
We introduce a novel approach for feature preserving mesh simplification based on vertex Laplacians, specifically, the uniformly weighted Laplacian. Our approach is unique in three aspects: 1) a Laplacian based shape descriptor to quantize the local geometric feature sensitivity; 2) a Laplacian weighted cost function that is capable of providing different retaining rates of the geometric features; and 3) an optimal clustering technique which combines K-means and the Laplacian based shape descriptor to implement vertex classification. During simplification, the Laplacian based shape descriptors are firstly computed, and then a chosen error function to be optimized is penalized by our Laplacian weighted cost function, leading it to feature preserving. By applying the clustering technique, different simplification operators may be applied to different vertex groups for different purposes. Different error functions have been implemented to demonstrate the effectiveness, applicability and flexibility of the approach. Experiments conducted on various models including those of natural objects and CAD ones, show superior results.
Year
DOI
Venue
2012
10.1007/978-3-642-34778-8_35
PCM
Keywords
Field
DocType
different error function,laplacian-based feature,local geometric feature sensitivity,weighted laplacian,geometric feature,chosen error function,shape descriptor,different vertex group,mesh simplification,different simplification operator,different purpose,laplacian weighted cost function,laplace operator,feature detection
CAD,Error function,Laplacian smoothing,Pattern recognition,Vertex (geometry),Feature detection,Computer science,Operator (computer programming),Artificial intelligence,Cluster analysis,Laplace operator
Conference
Citations 
PageRank 
References 
0
0.34
8
Authors
4
Name
Order
Citations
PageRank
Lin Zhang114616.93
Zhen Ma200.34
Zhong Zhou331844.52
Wei Wu4287.22