An adaptive moving least squares method for non-uniform points set fitting

Authors:
Xianping Huang;Qing Tian;Jianfei Mao
Affiliations:
College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou;College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou;College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou
Venue:
ACACOS'10 Proceedings of the 9th WSEAS international conference on Applied computer and applied computational science
Year:
2010

Citing 11
Cited 0

Surface reconstruction from unorganized points

SIGGRAPH '92 Proceedings of the 19th annual conference on Computer graphics and interactive techniques
The approximation power of moving least-squares

Mathematics of Computation
Meshless parameterization and surface reconstruction

Computer Aided Geometric Design
Reconstruction and representation of 3D objects with radial basis functions

Proceedings of the 28th annual conference on Computer graphics and interactive techniques
Point set surfaces

Proceedings of the conference on Visualization '01
Efficient simplification of point-sampled surfaces

Proceedings of the conference on Visualization '02
Multi-level partition of unity implicits

ACM SIGGRAPH 2003 Papers
Shape modeling with point-sampled geometry

ACM SIGGRAPH 2003 Papers
Error bounds and optimal neighborhoods for MLS approximation

SGP '06 Proceedings of the fourth Eurographics symposium on Geometry processing
Data-dependent MLS for faithful surface approximation

SGP '07 Proceedings of the fifth Eurographics symposium on Geometry processing
A survey of methods for moving least squares surfaces

SPBG'08 Proceedings of the Fifth Eurographics / IEEE VGTC conference on Point-Based Graphics

Quantified Score

Hi-index	0.01

Visualization

Abstract

This paper presents an adaptive algorithm based on the moving least squares methods (MLS) for Non-uniform sampled points fitting . The size of radius for MLS can be adaptively adjusted according to the consistency of the sampled data points. Experiments show that the method has more approximate fitting results than the traditional MLS.