Surface Mesh Smoothing, Regularization and Feature Detection

 

Surface Mesh Smoothing, Regularization and Feature Detection

Hui Huang1    Uri Ascher 1

   1University of British Columbia    

 

Figure 1: Recovering curved ridges on the fandisk model: (a) noisy model; (b) smoothed model based on the vertex partition in Figure 5(g); (c) smoothed model based on the refined vertex partition in Figure 5(h).

Abstract
We describe a hybrid algorithm that is designed to reconstruct a piecewise smooth surface mesh from noisy input. While denoising, our method simultaneously regularizes triangle meshes on flat regions for further mesh processing and preserves crease sharpness for faithful reconstruction. A clustering technique, which combines K-means and geometric a priori information, is first developed and refined. It is then used to implement vertex classification so that we can not only apply different smoothing operators on different vertex groups for different purposes, but also succeed in crease detection, where the tangent plane of the surface is discontinuous, without any significant cost increase. Consequently we are capable of efficiently obtaining different mesh segmentations, depending on user input and thus suitable for various applications.


BibTex
@ARTICLE{Huang2008
title = {Surface Mesh Smoothing, Regularization and Feature Detection},
author = {Hui Huang and Uri Ascher},
journal = {SIAM Journal on Scientific Computing},
volume = {31},
issue = {1},
pages = {74 - 93},
year = {2008},
}
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