Surface Mesh Smoothing, Regularization, and Feature Detection

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. Key words: Multiscale Anisotropic Laplacian, Umbrella Operator, Mesh Regularization, Crease Detection, Mesh Segmentation, K-means Clustering.

• Type: Journal
• Year: 2008
• Where: SIAMSC
• Authors: Hui Huang, Uri M. Ascher
• Keywords: Crease Detection, Mesh, Mesh Segmentation, SIAMSC 2008