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Anisotropic filtering on normal field and curvature tensor field using optimal estimation theory
Lyon, France June 13-June 15
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/SMI.2007.5IEEE International Conference on Shap ...
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Min Liu, Purdue University
Yushen Liu, Purdue University
Karthik Ramani, Purdue University
In this paper, we study the problem of mesh denoising for improving the single pass surface estimation on normals and curvature tensors. We focus mainly on the engineering objects represented as dense triangle meshes. In particular, a two run non-linear diffusion algorithm based on optimal estimation theory is proposed to adaptively filter out the undesired discontinuities introduced by noise while preserving the underlying features. We show that the proposed filter can successfully improve the local surface estimates while preserving the desired features in terms of tangential and curvature discontinuities.
Citation:
Min Liu, Yushen Liu, Karthik Ramani, "Anisotropic filtering on normal field and curvature tensor field using optimal estimation theory," smi, pp.169-178, IEEE International Conference on Shape Modeling and Applications 2007 (SMI '07), 2007
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