Abstract
Finely captured meshes often contain details like sharp edges, corners and shallow features. In order to improve the quality of these meshes, we present a robust and efficient high-resolution details-preserving mesh denoising algorithm. Our method consists of three stages. For each triangular face and its face neighborhood, we improve a robust density-based clustering method and apply it to the face neighborhood to extract a subset of neighbors which belong to the same cluster as the central face. And then, we filter the central face normal iteratively within this subset to remove noise. Because the faces within the extracted subset are not distributed across high-resolution details, our normal filtering can preserve such details as much as possible. Finally, we update the vertex positions to be consistent with the filtered face normals using a least-squares formulation. Experiments on various types of meshes indicate that our method has advantages over previous surface denoising methods.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Fan H, Peng Q, Yu Y. A robust algorithm for denoising meshes with high-resolution details. In: Proceedings of Computational Visual Media Conference, Beijing, 2012. 75–82
Field D. Laplacian smoothing and Delaunay triangulations. Commun Appl Numer Method, 1988, 4: 709–712
Taubin G. A signal processing approach to fair surface design. In: Proceedings of the 22nd Annual Conference on Computer Graphics and Interactive Techniques, New York, 1995. 351–358
Desbrun M, Meyer M, Schröder P, et al. Implicit fairing of irregular meshes using diffusion and curvature flow. In: Proceedings of the 26th Annual Conference on Computer Graphics and Interactive Techniques, New York, 1999. 317–324
Ohtake Y, Belyaev A, Bogaevski I. Polyhedral surface smoothing with simultaneous mesh regularization. In: Proceedings of the Geometric Modeling and Processing, Theory and Applications. Washington DC: IEEE Computer Society, 2000. 229–237
Clarenz U, Diewald U, Rumpf M. Anisotropic geometric diffusion in surface processing. In: Proceedings of the Conference on Visualization, Salt Lake City, 2000. 397–405
Desbrun M, Meyer M, Schröder P, et al. Anisotropic feature-preserving denoising of height fields and bivariate data. Graphic Interface, 2000, 11: 145–152
Bajaj C, Xu G. Anisotropic diffusion of surfaces and functions on surfaces. ACM Trans Graph, 2003, 22: 4–32
Fleishman S, Drori I, Cohen-Or D. Bilateral mesh denoising. In: Proceedings of the International Conference on Computer Graphics and Interactive Techniques, New York, 2003. 950–953
Jones T, Durand F, Desbrun M. Non-iterative, feature-preserving mesh smoothing. ACM Trans Graph, 2003, 22: 943–949
Tomasi C, Manduchi R. Bilateral filtering for gray and color images. In: Proceedings of the 6th International Conference on Computer Vision, Bombay, 1998. 839–846
Barash D. Fundamental relationship between bilateral filtering, adaptive smoothing, and the nonlinear diffusion equation. IEEE Trans Patt Anal Mach Intell, 2002, 24: 844–847
Ester M, Kriegel H, Sander J, et al. A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, Portland, 1996. 226–231
Durand F, Dorsey J. Fast bilateral filtering for the display of high-dynamic-range images. In: Proceedings of the 29th Annual Conference on Computer Graphics and Interactive Techniques, San Antonio, 2002. 257–266
Hildebrandt K, Polthier K. Anisotropic filtering of non-linear surface features. Comput Graph Forum, 2004, 23: 391–400
Fan H Q, Yu Y Z, Peng Q S. Robust feature-preserving mesh denoising based on consistent subneighborhoods. IEEE Trans Visual Comput Graph, 2010, 16: 312–324
Yagou H, Ohtake Y, Belyaev A. Mesh denoising via iterative alpha-trimming and nonlinear diffusion of normals with automatic thresholding. In: Proceedings of the Computer Graphics International, Tokyo, 2003. 28–33
Yu Y Z, Zhou K, Xu D et al. Mesh editing with poisson-based gradient field manipulation. ACM Trans Graph, 2004, 23: 644–651
Lee K, Wang W. Feature-preserving mesh denoising via bilateral normal filtering. In: Proceedings of the Ninth International Conference on Computer Aided Design and Computer Graphics, HongKong, 2005. 6
Sun X, Rosin P, Martin R, et al. Fast and effective feature-preserving mesh denoising. IEEE Trans Visual Comput Graph, 2007, 13: 925–938
Wang J, Yu Z. Feature-preserving mesh denoising via anisotropic surface fitting. J Comput Sci Technol, 2012, 27: 163–173
Shen Y, Barner K. Fuzzy vector median-based surface smoothing. IEEE Trans Visual Comput Graph, 2004, 10: 252–265
Taubin G. Linear Anisotropic Mesh Filtering. IBM Reserch Report RC2213. 2001
Belyaev A, Ohtake Y. A comparison of mesh smoothing methods. In: Israel-Korea Bi-National Conference on Geometric Modeling and Computer Graphics, Tel-Aviv, 2003. 83–87
Bouguet J, Perona P. 3D photography on your desk. In: Proceedings of the 6th International Conference on Computer Vision, Bombay, 1998. 43–50
Intel. Intel(R) 64 and IA-32 Architectures Optimization Reference Manual. 2013
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Fan, H., Peng, Q. & Yu, Y. A robust high-resolution details preserving denoising algorithm for meshes. Sci. China Inf. Sci. 56, 1–12 (2013). https://doi.org/10.1007/s11432-013-4993-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11432-013-4993-4