Multimedia Tools and Applications

, Volume 77, Issue 17, pp 23009–23021 | Cite as

Feature-preserving mesh denoising based on guided normal filtering

  • Shaohui LiuEmail author
  • Seungmin Rho
  • Renjie Wang
  • Feng Jiang


In order to robustly perform tasks based on 3D data model, we propose a feature-preserving mesh denoising algorithm based on the face classification. In the proposed algorithm, the sharp features which play a key role in 3D models are kept unchanged while denoising. The multiscale tensor voting is used to classify the faces into two classes where one is called as feature faces and another as non-feature faces. Feature faces is usually distributed in the neighbourhood of shape edges. Thus these feature faces are key faces in perceptual quality. For processing the faces more efficiently, we propose a search algorithm to find those faces which are close to the feature face and are of similar geometrical properties and then use them to guide the filtering process. The remaining faces are processed by an iteratively joint bilateral filtering. Finally, vertex position is updated according to the filtered face normals. the effectiveness of proposed approach is validated through extensive experiments. Experimental results show the performance is better than the existing methods.


Mesh denoising Feature face Joint bilateral filtering Feature-preserving Partial neighbor 



This work is partially funded by the Major State Basic Research Development Program of China (973 Program 2015CB351804), the Science and Technology Commission of China No.17-H863-03-ZT-003-010-01 and the Natural Science Foundation of China under Grant No. 61572155 and 61672188.


  1. 1.
    Anastasia I, Elisavet C, Spiros N, Ioannis K (2017) Deep learning advances in computer vision with 3D data: a survey. ACM Comput Surv 50(2):1–38Google Scholar
  2. 2.
    Bian Z, Tong R (2011) Feature-preserving mesh denoising based on vertices classification. Comput Aided Geom Des 28(1):50–64MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Fan H, Yu Y, Peng Q (2009) Robust feature-preserving mesh denoising based on consistent subneighborhoods. IEEE Trans Vis Comput Graph 16(2):312–324Google Scholar
  4. 4.
    Fleishman S, Drori I, Cohen-Or D (2003) Bilateral mesh denoising. Acm Trans Graph 22(3):950–953CrossRefGoogle Scholar
  5. 5.
    Gao Y, Zhang HW, Zhao XB, Yan SC (2017) Event classification in microblogs via social tracking. Acm Trans Intell Syst Technol 8(3):1–14CrossRefGoogle Scholar
  6. 6.
    He L, Schaefer S (2013) Mesh denoising via l0 minimization. Acm Trans Graph 32(4):1–8zbMATHGoogle Scholar
  7. 7.
    Jones TR, Durand FR, Desbrun M (2003) Non-iterative, feature-preserving mesh smoothing. Acm Trans Graph 22(3):943–949CrossRefGoogle Scholar
  8. 8.
    Kim HS, Choi HK, Lee KH (2009) Feature detection of triangular meshes based on tensor voting theory. Comput-Aided Des 41(1):47–58CrossRefGoogle Scholar
  9. 9.
    Lee KW, Wang WP (2006) Feature-preserving mesh denoising via bilateral normal filtering. Int Conf Comput Aided Des Comput Graph 1:275–280Google Scholar
  10. 10.
    Manduchi R, Tomasi C (1998) Bilateral Filtering for Gray and Color Images. IEEE Int Conf Comput Vis Pattern Recognit, pp 839–846Google Scholar
  11. 11.
    Peratham W, Douglas S, Cornelia F, Yiannis A (2016) Computer visioin and natural langurage processing: recent approaches in multimedia and robotics. ACM Comput Surv 49(4):1–44Google Scholar
  12. 12.
    Su LF, Gao Y, Zhao XB, Wan H, Gu M, Sun JG (2017) Vertex-weighted hypergraph learning for multi-view object classification. Proc IJCAI:2779–2785Google Scholar
  13. 13.
    Sun X, Rosin P, Martin R, Langbein F (2007) Fast and effective feature-preserving mesh denoising. IEEE Trans Vis Comput Graph 13(5):925–938CrossRefGoogle Scholar
  14. 14.
    Sun X, Rosin PL, Martin RR, Langbein FC (2008) Random walks for feature-preserving mesh denoising. Comput Aided Geom Des 25(7):437–456MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Wang J, Zhang X, Yu Z (2012) A cascaded approach for feature-preserving surface mesh denoising. Comput-Aided Des 44(7):597–610CrossRefGoogle Scholar
  16. 16.
    Wang FL, Qi SH, Gao G, Zhao SC, Wang XY (2016) Logo information recognition in large-scale social media data. Multimed Syst 22(1):63–73CrossRefGoogle Scholar
  17. 17.
    Wang R, Zhao W, Liu S, Zhao D, Liu C (2017) Feature-preserving mesh denoising based on guided normal filtering. The Pacific-Rim Conference on Multimedia (PCM 2017), HarbinGoogle Scholar
  18. 18.
    Wei M (2015) Bi-normal filtering for mesh denoising. IEEE Trans Vis Comput Graph 21(1):43–55CrossRefGoogle Scholar
  19. 19.
    Wei M, Liang L, Pang WM, Wang J, Li W, Wu H (2017) Tensor voting guided mesh denoising. IEEE Trans Autom Sci Eng 14(2):931–945CrossRefGoogle Scholar
  20. 20.
    Zhang Y et al (2010) Bilateral normal filtering for mesh denoising. IEEE Trans Vis Comput Graph 17(10):1521–1530CrossRefGoogle Scholar
  21. 21.
    Zhang W, Deng B, Zhang J, Bouaziz S, Liu L (2015) Guided mesh normal filtering. Comput Graph Forum 34(7):23–34CrossRefGoogle Scholar
  22. 22.
    Zhao SC, Chen L, Yao H, Zhang Y, Sun X (2015) Strategy for dynamic 3D depth data matching towards robust action retrieval. Neurocomputing 151:Part2, 533–543Google Scholar
  23. 23.
    Zhao SC, Yao H, Jiang XL (2015) Predicting continuous probability distribution of image emotions in valence-arousal space. Proceedings of ACM MM, AustraliaCrossRefGoogle Scholar
  24. 24.
    Zhao SC, Yao H, Jiang XL, Sun X (2015) Predicting discrete probability distribution of image emotionsGoogle Scholar
  25. 25.
    Zhao SC, Yao H, Zhang YH, Wang YS, Liu S (2015) View-based 3D object retrieval via multi-modal graph learning, signal processing 112(C):110–118Google Scholar
  26. 26.
    Zhao SC, Yao H, Gao Y, Ji RR, Xie WL, Jiang XL, Chua TS (2016) Predicting personalized emotion perceptions of social images. Proceedings of ACM MM, The NetherlandsCrossRefGoogle Scholar
  27. 27.
    Zhao SC, Yao H, Gao Y, Ding GG, Chua TS (2016) Predicting personalized image emotion perceptions in social networks. ieee transactions on affective computing,
  28. 28.
    Zhao SC, Ding GG, Gao Y, Han JG (2017) Approximating discrete probability distribution of image emotions by multi-modal features fusion. Proceedigns of IJCAI 2017 1:4669–4675Google Scholar
  29. 29.
    Zhao SC, Ding GG, Gao Y, Han JG (2017) Learning visual emotion distributions via multi-modal features fusion. Proc ACM MM, pp 369–377Google Scholar
  30. 30.
    Zhao SC, Gao Y, Han GG Ding, Chua TS (2017) Real-time multimedia social event detection in microblog. IEEE transactions on cybernetics,, online available Oct. 2017
  31. 31.
    Zhao SC, Yao H, Gao Y, Ji RR, Ding GG (2017) Continuous probability distribution prediction of image emotions via multitask shared sparse regression. IEEE Trans Multimed 19(3):632–645CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Computer Science and TechnologyHarbin Institute of TechnologyHarbinChina
  2. 2.Department of Media SoftwareSungkyul UniversitySungkyulKorea

Personalised recommendations