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SURFACE MESH SMOOTHING, REGULARIZATION, AND FEATURE DETECTION
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AI
DOI：10.1137/060676684.png)
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Abstract
En 中文
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.
Keywords:
multiscale anisotropic Laplacian
umbrella operator
mesh regularization
crease detection
mesh segmentation
K-means clustering
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