Abstract
We present a 3D mesh denoising method based on kernel density estimation. The proposed approach is able to reduce the oversmoothing effect and effectively remove undesirable noise while preserving prominent geometric features of a 3D mesh such as curved surface regions, sharp edges, and fine details. The experimental results demonstrate the effectiveness of the proposed approach in comparison to existing mesh denoising techniques.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Taubin, G.: A signal processing approach to fair surface design. In: Proc. ACM SIGGRAPH, pp. 351–358 (1995)
Ohtake, Y., Belyaev, A.G., Bogaevski, I.A.: Polyhedral surface smoothing with simultaneous mesh regularization. In: Proc. Geom. Modeling and Processing, pp. 229–237 (2000)
Taubin, G.: Linear anisotropic mesh filtering, IBM Research Report, RC2213 (2001)
Petitjean, S.: A survey of methods for recovering quadrics in triangle meshes. ACM Computing Surveys 34(2), 211–262 (2002)
Shen, Y., Barner, K.E.: Fuzzy Vector Median-Based Surface Smoothing. IEEE Trans. Visualization and Computer Graphics 10(3), 252–265 (2004)
Yagou, H., Ohtake, Y., Belyaev, A.: Mesh smoothing via mean and median filtering applied to face normals. In: Proc. Geometric Modeling and Processing - Theory and Applications, pp. 124–131 (2002)
Fleishman, S., Drori, I., Cohen-Or, D.: Bilateral mesh denoising. In: Proc. ACM SIGGRAPH, pp. 950–953 (2003)
Jones, T., Durand, F., Desbrun, M.: Non-iterative, feature preserving mesh smoothing. In: Proc. ACM SIGGRAPH, pp. 943–949 (2003)
Desbrun, M., Meyer, M., Schröder, P., Barr, A.: Implicit fairing of irregular meshes using diffusion and curvature flow. In: Proc. ACM SIGGRAPH, pp. 317–324 (1999)
Desbrun, M., Meyer, M., Schröder, P., Bar, A.: Anisotropic feature-preserving denoising of height fields and bivariate data. In: Graphics Interface, pp. 145–152 (2000)
Clarenz, U., Diewald, U., Rumpf, M.: Anisotropic geometric diffusion in surface processing. In: Proc. IEEE Visualization, pp. 397–405 (2000)
Clarenz, U., Diewald, U., Rumpf, M.: Processing textured surfaces via anisotropic geometric diffusion. IEEE Trans. Image Processing 13(2), 248–261 (2004)
Bajaj, C.L., Xu, G.: Anisotropic diffusion of subdivision surfaces and functions on surfaces. ACM Trans. Graphics 22(1), 4–32 (2003)
Tasdizen, T., Whitaker, R., Burchard, P., Osher, S.: Geometric surface smoothing via anisotropic diffusion of normals. In: Proc. IEEE Visualization, pp. 125–132 (2002)
Tasdizen, T., Whitaker, R., Burchard, P., Osher, S.: Geometric surface processing via normal maps. ACM Transactions on Graphics 22(4), 1012–1033 (2003)
Hildebrandt, K., Polthier, K.: Anisotropic filtering of non-linear surface features. Computer Graphics Forum 23(3), 391–400 (2004)
Chung, F.R.: Spectral Graph Theory. American Mathematical Society, Providence (1997)
Silverman, B.W.: Density estimation for statistics and data analysis. Chapman and Hall, London (1986)
Karni, Z., Gotsman, C.: Spectral compression of mesh geometry. In: Proc. ACM SIGGRAPH, pp. 279–286 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Tarmissi, K., Ben Hamza, A. (2009). Geometric Mesh Denoising via Multivariate Kernel Diffusion. In: Gagalowicz, A., Philips, W. (eds) Computer Vision/Computer Graphics CollaborationTechniques. MIRAGE 2009. Lecture Notes in Computer Science, vol 5496. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01811-4_3
Download citation
DOI: https://doi.org/10.1007/978-3-642-01811-4_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01810-7
Online ISBN: 978-3-642-01811-4
eBook Packages: Computer ScienceComputer Science (R0)