Abstract
We present an algorithm for denoising triangulated models based on L0 minimization. Our method maximizes the flat regions of the model and gradually removes noise while preserving sharp features. As part of this process, we build a discrete differential operator for arbitrary triangle meshes that is robust with respect to degenerate triangulations. We compare our method versus other anisotropic denoising algorithms and demonstrate that our method is more robust and produces good results even in the presence of high noise.
Supplemental Material
- Bajaj, C. L., and Xu, G. 2003. Anisotropic diffusion of surfaces and functions on surfaces. ACM Trans. Graph. 22, 1, 4--32. Google ScholarDigital Library
- Candes, E., Romberg, J., and Tao, T. 2006. Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Transactions on Information Theory 52, 2, 489--509. Google ScholarDigital Library
- Clarenz, U., Diewald, U., and Rumpf, M. 2000. Anisotropic geometric diffusion in surface processing. VIS, 397--405. Google ScholarDigital Library
- Desbrun, M., Meyer, M., Schröder, P., and Barr, A. H. 1999. Implicit fairing of irregular meshes using diffusion and curvature flow. SIGGRAPH, 317--324. Google ScholarDigital Library
- Desbrun, M., Meyer, M., Schröder, P., and Barr, A. H. 2000. Anisotropic feature-preserving denoising of height fields and bivariate data. Graphics Interface, 145--152.Google Scholar
- Donoho, D. 2006. Compressed sensing. IEEE Transactions on Information Theory 52, 4, 1289--1306. Google ScholarDigital Library
- Dziuk, G. 1990. An algorithm for evolutionary surfaces. Numerische Mathematik 58, 1, 603--611.Google ScholarDigital Library
- El Ouafdi, A. F., Ziou, D., and Krim, H. 2008. A smart stochastic approach for manifolds smoothing. In Proceedings of the Symposium on Geometry Processing, 1357--1364. Google ScholarDigital Library
- Fan, H., Yu, Y., and Peng, Q. 2010. Robust feature-preserving mesh denoising based on consistent subneighborhoods. IEEE Trans. Vis. Comp. Graph. 16, 2, 312--324. Google ScholarDigital Library
- Fleishman, S., Drori, I., and Cohen-Or, D. 2003. Bilateral mesh denoising. SIGGRAPH, 950--953. Google ScholarDigital Library
- Floater, M., Hormann, K., and Kos, G. 2006. A general construction of barycentric coordinates over convex polygons. Advances in Comp. Math 24, 311--331.Google ScholarCross Ref
- Hildebrandt, K., and Polthier, K. 2004. Anisotropic filtering of non-linear surface features. Computer Graphis Forum 23, 3, 391--400.Google ScholarCross Ref
- Jones, T. R., Durand, F., and Desbrun, M. 2003. Non-iterative, feature-preserving mesh smoothing. SIGGRAPH, 943--949. Google ScholarDigital Library
- Kazhdan, M., Solomon, J., and Ben-Chen, M. 2012. Can mean-curvature flow be modified to be non-singular? Computer Graphics Forum 31, 5, 1745--1754. Google ScholarDigital Library
- Kim, B., and Rossignac, J. 2005. Geofilter: Geometric selection of mesh filter parameters. Computer Graphis Forum 24, 3, 295--302.Google ScholarCross Ref
- Lee, K.-W., and Wang, W.-P. 2005. Feature-preserving mesh denoising via bilateral normal filtering. In Proceedings of Computer Aided Design and Computer Graphics, 275--280. Google ScholarDigital Library
- Levoy, M., Pulli, K., Curless, B., Rusinkiewicz, S., Koller, D., Pereira, L., Ginzton, M., Anderson, S., Davis, J., Ginsberg, J., Shade, J., and Fulk, D. 2000. The digital michelangelo project: 3d scanning of large statues. SIGGRAPH, 131--144. Google ScholarDigital Library
- Lipman, Y., Cohen-Or, D., Levin, D., and Tal-Ezer, H. 2007. Parameterization-free projection for geometry reconstruction. ACM Trans. Graph. 26, 3, 22:1--22:5. Google ScholarDigital Library
- Liu, X., Bao, H., Shum, H.-Y., and Peng, Q. 2002. A novel volume constrained smoothing method for meshes. Graphical Models 64, 169--182. Google ScholarDigital Library
- Mallet, J.-L. 1989. Discrete smooth interpolation. ACM Trans. Graph. 8, 2, 121--144. Google ScholarDigital Library
- Nealen, A., Igarashi, T., Sorkine, O., and Alexa, M. 2006. Laplacian mesh optimization. GRAPHITE, 381--389. Google ScholarDigital Library
- Pinkall, U., Juni, S. D., and Polthier, K. 1993. Computing discrete minimal surfaces and their conjugates. Experimental Mathematics 2, 15--36.Google ScholarCross Ref
- Schaefer, S., Ju, T., and Warren, J. 2007. A unified, integral construction for coordinates over closed curves. Computer Aided Geometric Design 24, 8-9, 481--493. Google ScholarDigital Library
- Shen, Y., and Barner, K. E. 2004. Fuzzy vector median-based surface smoothing. IEEE Trans. Vis. Comp. Graph. 10, 3, 252--265. Google ScholarDigital Library
- Sivan Toledo, D. C., and Rotkin, V. 2001. Taucs: A library of sparse linear solvers.Google Scholar
- Su, Z., Wang, H., and Cao, J. 2009. Mesh denoising based on differential coordinates. Shape Modeling International, 1--6.Google Scholar
- Sun, X., Rosin, P. L., Martin, R. R., and Langbein, F. C. 2007. Fast and effective feature-preserving mesh denoising. IEEE Trans. Vis. Comp. Graph., 925--938. Google ScholarDigital Library
- Sun, X., Rosin, P. L., Martin, R. R., and Langbein, F. C. 2008. Random walks for feature-preserving mesh denoising. Computer Aided Geometric Design 25, 7, 437--456. Google ScholarDigital Library
- Tasdizen, T., Whitaker, R., Burchard, P., and Osher, S. 2002. Geometric surface smoothing via anisotropic diffusion of normals. VIS, 125--132. Google ScholarDigital Library
- Taubin, G. 1995. A signal processing approach to fair surface design. SIGGRAPH, 351--358. Google ScholarDigital Library
- Taubin, G. 2001. Linear anisotropic mesh filtering. IBM Research Report RC22213(W0110-051).Google Scholar
- Tomasi, C., and Manduchi, R. 1998. Bilateral filtering for gray and color images. In Proceedings of the Sixth International Conference on Computer Vision, 839--846. Google ScholarDigital Library
- Tschumperlé, D. 2006. Fast anisotropic smoothing of multivalued images using curvature-preserving pde's. Int. J. Comput. Vision 68, 1, 65--82. Google ScholarDigital Library
- Vollmer, J., Mencl, R., and Mller, H. 1999. Improved laplacian smoothing of noisy surface meshes. Computer Graphics Forum 18, 3, 131--138.Google ScholarCross Ref
- Wang, C. C. L. 2006. Bilateral recovering of sharp edges on feature-insensitive sampled meshes. IEEE Trans. Vis. Comp. Graph. 12, 4, 629--639. Google ScholarDigital Library
- Xu, L., Lu, C., Xu, Y., and Jia, J. 2011. Image smoothing via l0 gradient minimization. ACM Trans. Graph. 30, 6, 174:1--174:12. Google ScholarDigital Library
- Yagou, H., Ohtake, Y., and Belyaev, A. 2002. Mesh smoothing via mean and median filtering applied to face normals. GMP, 124--131. Google ScholarDigital Library
- Yagou, H., Ohtake, Y., and Belyaev, A. G. 2003. Mesh denoising via iterative alpha-trimming and nonlinear diffusion of normals with automatic thresholding. Computer Graphics International Conference, 28--33.Google Scholar
- Zheng, Y., Fu, H., Au, O. K.-C., and Tai, C.-L. 2011. Bilateral normal filtering for mesh denoising. IEEE Trans. Vis. Comp. Graph. 17, 10, 1521--1530. Google ScholarDigital Library
Index Terms
- Mesh denoising via L0 minimization
Recommendations
Bilateral mesh denoising
We present an anisotropic mesh denoising algorithm that is effective, simple and fast. This is accomplished by filtering vertices of the mesh in the normal direction using local neighborhoods. Motivated by the impressive results of bilateral filtering ...
Bilateral mesh denoising
SIGGRAPH '03: ACM SIGGRAPH 2003 PapersWe present an anisotropic mesh denoising algorithm that is effective, simple and fast. This is accomplished by filtering vertices of the mesh in the normal direction using local neighborhoods. Motivated by the impressive results of bilateral filtering ...
Mesh denoising via cascaded normal regression
We present a data-driven approach for mesh denoising. Our key idea is to formulate the denoising process with cascaded non-linear regression functions and learn them from a set of noisy meshes and their ground-truth counterparts. Each regression ...
Comments