[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Springer Nature Link
Log in

Segmentation-driven feature-preserving mesh denoising

  • Original article
  • Published:
The Visual Computer Aims and scope Submit manuscript

Abstract

Feature-preserving mesh denoising has received noticeable attention in visual media, with the aim of recovering high-fidelity, clean mesh shapes from the ones that are contaminated by noise. Existing denoising methods often design smaller weights for anisotropic surfaces and larger weights for isotropic surfaces in order to preserve sharp features, such as edges or corners, on the mesh shapes. However, they often disregard the fact that such small weights on anisotropic surfaces still pose negative impacts on the denoising outcomes and detail preservation results on the shapes. In this paper, we propose a novel segmentation-driven mesh denoising method which performs region-wise denoising, and thus avoids the disturbance of anisotropic neighbour faces for better feature preservation results. Also, our backbone can be easily embedded into commonly used mesh denoising frameworks. Extensive experiments have demonstrated that our method can enhance the denoising results on a wide range of synthetic and real mesh models, both quantitatively and visually.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Algorithm 1
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25
Fig. 26
Fig. 27
Fig. 28
Fig. 29
Fig. 30

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

Availability of data and materials

The test mesh data set being presented in this paper is available on reasonable request.

References

  1. Ströter, D., Mueller-Roemer, J.S., Weber, D., Fellner, D.W.: Fast harmonic tetrahedral mesh optimization. Vis. Comput. 38(9), 3419–3433 (2022). https://doi.org/10.1007/s00371-022-02547-6

    Article  Google Scholar 

  2. Jia, S., Zhang, W., Wang, G., Pan, Z., Yu, X.: A real-time deformable cutting method using two levels of linked voxels for improved decoupling between collision and rendering. Vis. Comput. 39(2), 765–783 (2022). https://doi.org/10.1007/s00371-021-02373-2

    Article  Google Scholar 

  3. Prashant, G.: A survey of modeling, rendering and animation of clouds in computer graphics. Vis. Comput. 37(7), 1931–1948 (2020). https://doi.org/10.1007/s00371-020-01953-y

    Article  Google Scholar 

  4. Sun, X., Rosin, P.L., Martin, R., Langbein, F.: Fast and effective feature-preserving mesh denoising. IEEE Trans. Vis. Comput. Graph. 13(5), 925–938 (2007)

    Article  Google Scholar 

  5. Zheng, Y., Fu, H., Au, O.K.C., Tai, C.L.: Bilateral normal filtering for mesh denoising. IEEE Trans. Vis. Comput. Graph. 17(10), 1521–1530 (2011)

    Article  Google Scholar 

  6. Zhang, W., Deng, B., Zhang, J., Bouaziz, S., Liu, L.: Guided mesh normal filtering. Comput. Graph. Forum 34(7), 23–34 (2015)

    Article  Google Scholar 

  7. Lu, X., Liu, X., Deng, Z., Chen, W.: An efficient approach for feature-preserving mesh denoising. Opt. Lasers Eng. 90, 186–195 (2017)

    Article  Google Scholar 

  8. Hildebrandt, K., Polthier, K.: Anisotropic filtering of non-linear surface features. Comput. Graph. Forum 23(3), 391–400 (2004)

    Article  Google Scholar 

  9. Lu, X., Schaefer, S., Luo, J., Ma, L., He, Y.: Low rank matrix approximation for 3d geometry filtering. IEEE Trans. Vis. Comput. Graph. 28(4), 1835–1847 (2022)

    Article  Google Scholar 

  10. Li, X., Zhu, L., Fu, C.W., Heng, P.A.: Non-local low-rank normal filtering for mesh denoising. Comput. Graph. Forum 37(7), 155–166 (2018)

    Article  Google Scholar 

  11. Yaz, I.O., Loriot, S.: Triangulated surface mesh segmentation. In: CGAL User and Reference Manual (2022). https://doc.cgal.org/5.4.1/Manual/packages.html#PkgSurfaceMeshSegmentation

  12. Grzeczkowicz, G., Vallet, B.: Semantic segmentation of urban textured meshes through point sampling. ISPRS Ann. Photogram., Remote Sens. Spat. Inf. Sci. 2, 177–184 (2022)

    Google Scholar 

  13. Hu, Z., Bai, X., Shang, J., Zhang, R., Dong, J., Wang, X., Sun, G., Fu, H., Tai, C.L.: Vmnet: voxel-mesh network for geodesic-aware 3d semantic segmentation. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 15488–15498 (2021)

  14. Lian, J., Li, H., Li, N., Cai, Q.: An adaptive mesh segmentation via iterative K-means clustering. In: Proceedings of 2021 Chinese Intelligent Systems Conference, pp. 193–201. Springer Singapore, Singapore (2022)

  15. Cohen-Steiner, D., Alliez, P., Desbrun, M.: Variational shape approximation. In: ACM SIGGRAPH 2004 Papers, pp. 905–914 (2004)

  16. Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Süsstrunk, S.: Slic superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2274–2282 (2012)

    Article  Google Scholar 

  17. Simari, P., Picciau, G., De Floriani, L.: Fast and scalable mesh superfacets. Comput. Graph. Forum 33(7), 181–190 (2014)

    Article  Google Scholar 

  18. Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 24(5), 603–619 (2002)

    Article  Google Scholar 

  19. Sheikh, Y.A., Khan, E.A., Kanade, T.: Mode-seeking by medoidshifts. In: 2007 11th IEEE International Conference on Computer Vision, pp. 1–8 (2007). https://doi.org/10.1109/ICCV.2007.4408978

  20. Vedaldi, A., Soatto, S.: Quick shift and kernel methods for mode seeking. In: Computer Vision – ECCV 2008, pp. 705–718. Springer Berlin Heidelberg, Berlin, Heidelberg (2008)

  21. Katz, S., Tal, A.: Hierarchical mesh decomposition using fuzzy clustering and cuts. ACM Trans. Graph. (TOG) 22(3), 954–961 (2003)

    Article  Google Scholar 

  22. Attene, M., Falcidieno, B., Spagnuolo, M.: Hierarchical mesh segmentation based on fitting primitives. Vis. Comput. 22(3), 181–193 (2006)

    Article  Google Scholar 

  23. Lai, Y.K., Hu, S.M., Martin, R.R., Rosin, P.L.: Fast mesh segmentation using random walks. In: Proceedings of the 2008 ACM Symposium on Solid and Physical Modeling, pp. 183–191 (2008)

  24. Golovinskiy, A., Funkhouser, T.: Randomized cuts for 3d mesh analysis. In: ACM SIGGRAPH Asia 2008 papers, pp. 1–12 (2008)

  25. Shapira, L., Shamir, A., Cohen-Or, D.: Consistent mesh partitioning and skeletonisation using the shape diameter function. Vis. Comput. 24(4), 249 (2008)

    Article  Google Scholar 

  26. Zheng, Y., Tai, C.L., Au, O.K.C.: Dot scissor: a single-click interface for mesh segmentation. IEEE Trans. Vis. Comput. Graph. 18(8), 1304–1312 (2011)

    Article  Google Scholar 

  27. Lee, Y., Lee, S., Shamir, A., Cohen-Or, D., Seidel, H.P.: Mesh scissoring with minima rule and part salience. Comput. Aided Geometr. Des. 22(5), 444–465 (2005)

    Article  MathSciNet  Google Scholar 

  28. Kalogerakis, E., Hertzmann, A., Singh, K.: Learning 3d mesh segmentation and labeling. In: ACM SIGGRAPH 2010 papers, pp. 1–12 (2010)

  29. Liu, C.M., Luan, W.N., Fu, R.H., Pang, H.B., Li, Y.H.: Attention-embedding mesh saliency. Vis. Comput. 39(5), 1783–1795 (2022)

    Article  Google Scholar 

  30. Garcia-Garcia, A., Orts-Escolano, S., Oprea, S., Villena-Martinez, V., Martinez-Gonzalez, P., Garcia-Rodriguez, J.: A survey on deep learning techniques for image and video semantic segmentation. Appl. Soft Comput. 70, 41–65 (2018)

    Article  Google Scholar 

  31. Vollmer, J., Mencl, R., Müller, H.: Improved Laplacian smoothing of noisy surface meshes. Comput. Graph. Forum 18(3), 131–138 (1999)

    Article  Google Scholar 

  32. Field, D.A.: Laplacian smoothing and Delaunay triangulations. Commun. Appl. Numer. Methods 4(6), 709–712 (1988)

    Article  Google Scholar 

  33. Liu, X., Bao, H., Shum, H.Y., Peng, Q.: A novel volume constrained smoothing method for meshes. Graph. Models 64(3–4), 169–182 (2002)

    Article  Google Scholar 

  34. Kim, B., Rossignac, J.: Geofilter: Geometric selection of mesh filter parameters. Comput. Graph. Forum 24(3), 295–302 (2005)

    Article  Google Scholar 

  35. Nehab, D., Rusinkiewicz, S., Davis, J., Ramamoorthi, R.: Efficiently combining positions and normals for precise 3d geometry. ACM Trans. Graph. 24(3), 536–543 (2005)

    Article  Google Scholar 

  36. Nealen, A., Igarashi, T., Sorkine, O., Alexa, M.: Laplacian mesh optimization. In: Proceedings of GRAPHITE’06, pp. 381–389 (2006)

  37. Su, Z.X., Wang, H., Cao, J.J.: Mesh denoising based on differential coordinates. In: Proc. of IEEE Int’l Conf. on Shape Modeling and Applications 2009, pp. 1–6 (2009)

  38. Lee, K.W., Wang, W.P.: Feature-preserving mesh denoising via bilateral normal filtering. In: Proc. of Int’l Conf. on Computer Aided Design and Computer Graphics (2005)

  39. Chen, S., Wang, J., Pan, W., Gao, S., Wang, M., Lu, X.: Towards uniform point distribution in feature-preserving point cloud filtering. arXiv:2201.01503 (2022)

  40. Wei, M., Yu, J., Pang, W., Wang, J., Qin, J., Liu, L., Heng, P.: Bi-normal filtering for mesh denoising. Vis. Comput. Graph., IEEE Trans. 21(1), 43–55 (2015)

    Article  Google Scholar 

  41. Fan, H., Yu, Y., Peng, Q.: Robust feature-preserving mesh denoising based on consistent subneighborhoods. IEEE Trans. Vis. Comput. Graph. 16(2), 312–324 (2010)

  42. Bian, Z., Tong, R.: Feature-preserving mesh denoising based on vertices classification. Comput. Aided Geometr. Des. 28(1), 50–64 (2011)

    Article  MathSciNet  Google Scholar 

  43. Wang, J., Zhang, X., Yu, Z.: A cascaded approach for feature-preserving surface mesh denoising. Comput.-Aided Des. 44(7), 597–610 (2012)

    Article  Google Scholar 

  44. Zhu, L., Wei, M., Yu, J., Wang, W., Qin, J., Heng, P.A.: Coarse-to-fine normal filtering for feature-preserving mesh denoising based on isotropic subneighborhoods. Comput. Graph. Forum 32(7), 371–380 (2013)

    Article  Google Scholar 

  45. Wang, J., Yu, Z.: A novel method for surface mesh smoothing: applications in biomedical modeling. In: Proceedings of the 18th International Meshing Roundtable, IMR 2009, October 25–28, 2009, Salt Lake City, UT, USA, pp. 195–210 (2009)

  46. Wei, M., Liang, L., Pang, W.M., Wang, J., Li, W., Wu, H.: Tensor voting guided mesh denoising. IEEE Trans. Autom. Sci. Eng. 14(2), 931–945 (2017)

    Article  Google Scholar 

  47. Lu, X., Deng, Z., Chen, W.: A robust scheme for feature-preserving mesh denoising. IEEE Trans. Vis. Comput. Graph. 22(3), 1181–1194 (2016)

    Article  Google Scholar 

  48. Lu, X., Chen, W., Schaefer, S.: Robust mesh denoising via vertex pre-filtering and 1-median normal filtering. Comput. Aided Geometr. Des. 54, 49–60 (2017)

    Article  MathSciNet  Google Scholar 

  49. He, L., Schaefer, S.: Mesh denoising via l0 minimization. ACM Trans. Graph 32(4), 64:1-64:8 (2013)

    Article  Google Scholar 

  50. Zhao, Y., Qin, H., Zeng, X., Xu, J., Dong, J.: Robust and effective mesh denoising using L0 sparse regularization. Comput.-Aided Des. 101, 82–97 (2018)

    Article  Google Scholar 

  51. Pan, W., Lu, X., Gong, Y., Tang, W., Liu, J., He, Y., Qiu, G.: Hlo: half-kernel Laplacian operator for surface smoothing. Comput.-Aided Des. 121, 102807 (2020)

    Article  MathSciNet  Google Scholar 

  52. Vieira, M., Shimada, K.: Surface mesh segmentation and smooth surface extraction through region growing. Comput. Aided Geometr. Des. 22(8), 771–792 (2005)

    Article  MathSciNet  Google Scholar 

  53. Huang, H., Ascher, U.: Surface mesh smoothing, regularization, and feature detection. SIAM J. Sci. Comput. 31(1), 74–93 (2008). https://doi.org/10.1137/060676684

    Article  MathSciNet  Google Scholar 

  54. Legrand, H., Thiery, J.M., Boubekeur, T.: Filtered quadrics for high-speed geometry smoothing and clustering. Comput. Graph. Forum 38(1), 663–677 (2019)

    Article  Google Scholar 

  55. Wang, C., Liu, Z., Liu, L.: Feature-preserving Mumford-Shah mesh processing via nonsmooth nonconvex regularization. Comput. Graph. 106, 222–236 (2022). https://doi.org/10.1016/j.cag.2022.06.006

  56. Liu, Z., Li, Y., Wang, W., Liu, L., Chen, R.: Mesh total generalized variation for denoising. IEEE Trans. Vis. Comput. Graph. 28(12), 4418–4433 (2021). https://doi.org/10.1109/TVCG.2021.3088118

    Article  Google Scholar 

  57. Cohen-Steiner, D., Alliez, P., Desbrun, M.: Variational shape approximation. ACM Trans. Graph. 23(3), 905–914 (2004)

Download references

Author information

Authors and Affiliations

  1. Deakin University, Geelong, VIC, Australia

    Weijia Wang, Richard Dazeley, Lei Wei & Bernard Rolfe

  2. OPT Machine Vision Tech Co., Ltd. (Japan), Tokyo, Japan

    Wei Pan

  3. OPT Machine Vision Tech Co., Ltd., Dongguan, China

    Chaofan Dai

  4. La Trobe University, Bundoora, VIC, Australia

    Xuequan Lu

Authors
  1. Weijia Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wei Pan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chaofan Dai
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Richard Dazeley
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Lei Wei
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Bernard Rolfe
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Xuequan Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Weijia Wang, Wei Pan or Xuequan Lu.

Ethics declarations

Conflict of interest

The authors declare that there are no conflicts of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Weijia Wang and Wei Pan are co-first authors.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, W., Pan, W., Dai, C. et al. Segmentation-driven feature-preserving mesh denoising. Vis Comput 40, 6201–6217 (2024). https://doi.org/10.1007/s00371-023-03161-w

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00371-023-03161-w

Keywords

Search

Navigation