Skip to main content
Springer Nature Link
Log in

Guided normal filter for 3D point clouds

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Point clouds have been attracting more and more attention due to the advancement of 3D sensors. However, the raw point clouds acquired suffer inevitably from noise, which challenges their applications in 3D computer vision. In order to address this problem, we propose a novel feature-preserving filtering framework, termed Guided Normal Point Cloud Filter. First, we perform initial normal estimation using improved Principal Component Analysis algorithm. Then, a well-designed point normal filter based on locally linear model is proposed, which uses the estimated normal field as guidance. Finally, according to the adjusted normal field, we treat the point positions update problem as a least-squares issue solved by stochastic gradient decent optimizer. Quantitative and qualitative experimental results on several point cloud models show the effectiveness of our proposed algorithm, which can provide a much better trade-off between filtering performance and computational efficiency.

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
$34.99 /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 excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Algorithm 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Alexa M, Behr J, Cohen Or D, Fleishman S, Levin D, Silva CT (2001) Point set surfaces. In: Proceedings Visualization, 2001. VIS’01. IEEE, pp 21–29

  2. Alexa M, Behr J, Cohen Or D, Fleishman S, Levin D, Silva CT (2003) Computing and rendering point set surfaces. IEEE Trans Vis Comput Graph 9(1):3–15

    Article  Google Scholar 

  3. Avron H, Sharf A, Greif C, Cohen Or D (2010) L1 sparse reconstruction of sharp point set surfaces. ACM Trans Graph (TOG) 29(5):1–12

    Article  Google Scholar 

  4. Bai B (2012) Analysis of 3d reconstruction of crime scene based on virtual reality technology. Tech Autom Appl (04):38–40

  5. Belhedi A, Bartoli A, Bourgeois S, Hamrouni K, Sayd P, Gay-Bellile V (2012) Noise modelling and uncertainty propagation for tof sensors. Springer, Berlin

    Book  Google Scholar 

  6. Boulch A, Marlet R (2012) Fast and robust normal estimation for point clouds with sharp features. In: Computer graphics forum, vol 31. Wiley Online Library, pp 1765–1774

  7. Brown BM (1983) Statistical uses of the spatial median. J R Stat Soc Ser B Methodol 45(1)

  8. Buades A, Coll B, Morel JM (2005) A non-local algorithm for image denoising. In: 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR’05), vol 2. IEEE, pp 60–65

  9. Dey T, Goswami S, Sun J (2004) Smoothing noisy point clouds with delaunay preprocessing and mls. The Ohio State University Technical Report No OSU-CISRC-3/04-TR17

  10. Fleishman S, Drori I, Cohen-Or D (2003) Bilateral mesh denoising. ACM Trans Graph 22(3)

  11. Fujiwara K, Hashimoto T (2020) Neural implicit embedding for point cloud analysis. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 11,734–11,743

  12. Guo Y, Wang H, Hu Q, Liu H, Liu L, Bennamoun M (2020) Deep learning for 3d point clouds: A survey. IEEE Trans Pattern Anal Mach Intell

  13. Han XF, Jin JS, Wang MJ, Jiang W (2018) Iterative guidance normal filter for point cloud. Multimed Tools Appl 77(13):16,887–16,902

    Article  Google Scholar 

  14. Han XF, Jin JS, Wang MJ, Jiang W, Gao L, Xiao L (2017) A review of algorithms for filtering the 3d point cloud. Signal Process Image Commun 57:103–112

    Article  Google Scholar 

  15. He K, Jian S, Tang X (2010) Guided image filtering. Springer, Berlin

    Book  Google Scholar 

  16. Huang H, Li D, Zhang H, Ascher U, Cohen-Or D (2009) Consolidation of unorganized point clouds for surface reconstruction. ACM Trans Graph (TOG) 28(5):1–7

    Article  Google Scholar 

  17. Huang H, Wu S, Gong M, Cohen-Or D, Ascher U, Zhang H (2013) Edge-aware point set resampling. ACM Trans Graph (TOG) 32(1):1–12

    Article  MATH  Google Scholar 

  18. Huang HY, Lin CL, Wu CY, Cheng YJ, Lin CH (2013) Metal organic framework-organic polymer monolith stationary phases for capillary electrochromatography and nano-liquid chromatography. Analytica Chim Acta 779(10):96–103

    Article  Google Scholar 

  19. Ito K (2002) Gaussian filter for nonlinear filtering problems. In: IEEE conference on decision & control

  20. Jones TR, Durand F, Desbrun M (2003) Non-iterative, feature-preserving mesh smoothing. ACM Trans Graph 22(3):943–949

    Article  Google Scholar 

  21. Jones TR, Durand F, Zwicker M (2004) Normal improvement for point rendering. IEEE Comput Graph Appl 24(4):53–56

    Article  Google Scholar 

  22. Lee K, Wang W (2006) Feature-preserving mesh denoising via bilateral normal filtering. In: 9th international conference on computer aided design and computer graphics, 2005

  23. Levin D (1998) The approximation power of moving least-squares. Math Comput 67(224):1517–1531

    Article  MathSciNet  MATH  Google Scholar 

  24. Levin D (2004) Mesh-independent surface interpolation. In: Geometric modeling for scientific visualization. Springer, pp 37–49

  25. Liao B, Xiao C, Jin L, Fu H (2013) Efficient feature-preserving local projection operator for geometry reconstruction. Comput Aided Des 45 (5):861–874

    Article  Google Scholar 

  26. Lin Y, Yan Z, Huang H, Du D, Liu L, Cui S, Han X (2020) Fpconv: Learning local flattening for point convolution. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 4293–4302

  27. Lipman Y, Cohen Or D, Levin D, Tal Ezer H (2007) Parameterization-free projection for geometry reconstruction. ACM Trans Graph (TOG) 26 (3):22–es

    Article  Google Scholar 

  28. Liu Z, Xiao X, Zhong S, Wang W, Li Y, Zhang L, Xie Z (2020) A feature-preserving framework for point cloud denoising. Comput-Aided Des 127:102,857. https://doi.org/10.1016/j.cad.2020.102857, https://www.sciencedirect.com/science/article/pii/S0010448520300506

    Article  MathSciNet  Google Scholar 

  29. Lu D, Lu X, Sun Y, Wang J (2020) Deep feature-preserving normal estimation for point cloud filtering. Comput Aided Des 125(102):860

    MathSciNet  Google Scholar 

  30. Lu X, Schaefer S, Luo J, Ma L, He Y (2020) Low rank matrix approximation for 3d geometry filtering. IEEE Trans Vis Comput Graph

  31. Lu X, Wu S, Chen H, Yeung SK, Chen W, Zwicker M (2017) Gpf: Gmm-inspired feature-preserving point set filtering. IEEE Trans Vis Comput Graph 24(8):2315–2326

    Google Scholar 

  32. Miropolsky A, Fischer A (2004) Reconstruction with 3D geometric bilateral filter. Reconstruction with 3D geometric bilateral filter

  33. Moorfield B, Haeusler R, Klette R (2015) Bilateral filtering of 3d point clouds for refined 3d roadside reconstructions. In: International conference on computer analysis of images and patterns

  34. Nasab SE, Ghaleh SF, Ramezanpour S, Kasaei S, Sanaei E (2014) Permutohedral lattice in 3d point cloud processing. In: 2014 7th international symposium on telecommunications (IST)

  35. Öztireli AC, Guennebaud G, Gross M (2009) Feature preserving point set surfaces based on nonlinear kernel regression. In: Computer graphics forum, vol 28. Wiley Online Library, pp 493–501

  36. Preiner R, Mattausch O, Arikan M, Pajarola R, Wimmer M (2014) Continuous projection for fast l1 reconstruction. ACM Trans Graph 33 (4):47–1

    Article  MATH  Google Scholar 

  37. Qiao PY, Xin HE, Wei ZH, Wang FY, Lin WC (2012) Application of gaussian filter in extracting bullet feature of 3-d bullet image. Chin J Liq Cryst Displays 27(5):708–712

    Article  Google Scholar 

  38. Rakotosaona MJ, La Barbera V, Guerrero P, Mitra NJ, Ovsjanikov M (2020) Pointcleannet: Learning to denoise and remove outliers from dense point clouds. In: Computer graphics forum, vol 39. Wiley Online Library, pp 185–203

  39. Rosli NAIM, Ramli A (2014) Mapping bootstrap error for bilateral smoothing on point set. In: AIP conference proceedings, vol 1605. American Institute of Physics, pp 149–154

  40. Schall O, Belyaev A, Seidel HP (2008) Adaptive feature-preserving non-local denoising of static and time-varying range data. Comput Aided Des 40 (6):701–707

    Article  Google Scholar 

  41. Sharma S, Raju A (2013) Application of gaussian filter with principal component analysis algorithm for the efficient face recognition. Int J Electron Commun Eng Technol

  42. Shi Z, Fu S, Lv X, Ke Q, Wang T (2019) Application of 3d point cloud registration algorithm in medical surgery. Electro-Optic Technol App v.34;No.168(05):57–61

    Google Scholar 

  43. Small CG (1990) A survey of multidimensional medians. Int Stat Rev / Revue Int Stat 58(3):263–277

    Article  Google Scholar 

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

    Article  Google Scholar 

  45. Sun X, Rosin PL, Martin RR, Langbein FC (2008) Noise in 3d laser range scanner data. In: IEEE international conference on shape modeling & applications

  46. Sun Y, Schaefer S, Wang W (2015) Denoising point sets via l0 minimization. Comput Aided Geom Des 35:2–15

    Article  MATH  Google Scholar 

  47. Tagliasacchi A, Zhang H, Cohen-Or D (2009) Curve skeleton extraction from incomplete point cloud. In: ACM SIGGRAPH 2009 papers, pp 1–9

  48. Tatsumi T, Kim M, Iba D, Hongu J, Moriwaki I (2016) Application of gaussian filter to measured helix form data: (elimination of pseudo roughness based on jis b 0632). Proc Mech Eng Congr Jpn 2016:S1170,507

    Article  Google Scholar 

  49. Tomasi C, Manduchi R (2002) Bilateral filtering for gray and color images. In: International conference on computer vision

  50. Wand M, Berner A, Bokeloh M, Jenke P, Fleck A, Hoffmann M, Maier B, Staneker D, Schilling A, Seidel HP (2008) Processing and interactive editing of huge point clouds from 3d scanners. Comput Graph 32(2):204–220. https://doi.org/10.1016/j.cag.2008.01.010

    Article  Google Scholar 

  51. Wang J, Xu K, Liu L, Cao J, Liu S, Yu Z, Gu XD (2013) Consolidation of low-quality point clouds from outdoor scenes. Comput Graph Forum 32(5):207–216

    Article  Google Scholar 

  52. Xu W, Lee IS, Lee SK, Bo L, Lee EJ (2015) Multiview-based hand posture recognition method based on point cloud. Ksii Trans Internet Inf Syst 9(7):2585–2598

    Google Scholar 

  53. Ye M, Wang X, Yang R, Ren L, Pollefeys M (2011) Accurate 3d pose estimation from a single depth image. In: 2011 international conference on computer vision. IEEE, pp 731–738

  54. Zhang J, Zhu C, Zheng L, Xu K (2020) Fusion-aware point convolution for online semantic 3d scene segmentation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 4534–4543

  55. Zhang YQ, Ding Y, Liu J, Guo Z (2013) Guided image filtering using signal subspace projection. IET Image Process 7(3):270–279

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

This research was supported by the National Natural Science Foundation of China (No. 62002299), and the Natural Science Foundation of Chongqing of China (No. cstc2020jcyj-msxmX0126), and the Fundamental Research Funds for the Central Universities (No. SWU120005).

Author information

Authors and Affiliations

  1. College of Computer and Information Science, Southwest University, Chongqing, China

    Zhi-Ao Feng & Xian-Feng Han

Authors
  1. Zhi-Ao Feng
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Xian-Feng Han
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Xian-Feng Han.

Ethics declarations

Conflict of Interests

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note

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

Rights and permissions

Springer Nature or its licensor 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

Feng, ZA., Han, XF. Guided normal filter for 3D point clouds. Multimed Tools Appl 82, 13797–13810 (2023). https://doi.org/10.1007/s11042-022-13751-w

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-022-13751-w

Keywords