3D CAD model search: a regularized manifold learning approach
ROBIO'09 Proceedings of the 2009 international conference on Robotics and biomimetics
Journal of Computational Physics
Distance-aware smoothing of surface meshes for surgical planning
Proceedings of the First International Workshop on Digital Engineering
Technical Section: Context-aware mesh smoothing for biomedical applications
Computers and Graphics
3D CAD model retrieval with perturbed Laplacian spectra
Computers in Industry
Staircase-aware smoothing of medical surface meshes
EG VCBM'10 Proceedings of the 2nd Eurographics conference on Visual Computing for Biology and Medicine
Edge-aware point set resampling
ACM Transactions on Graphics (TOG)
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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.