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3D point cloud denoising using anisotropic neighborhoods and a novel sharp feature detection algorithm

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Abstract

3D point cloud denoising is a fundamental task in a geometry-processing pipeline, where feature preservation is essential for various applications. The literature presents several methods to overcome the denoising problem; however, most of them focus on denoising smooth surfaces and not on handling sharp features correctly. This paper proposes a new sharp feature-preserving method for point cloud denoising that incorporates solutions for normal estimation and feature detection. The denoising method consists of four major steps. First, we compute the per-point anisotropic neighborhoods by solving local quadratic optimization problems that penalize normal variation. Second, we estimate a piecewise smooth normal field that enhances sharp feature regions using these anisotropic neighborhoods. This step includes bilateral filtering and a novel corrector procedure to obtain more reliable normals for the subsequent steps. Third, we employ a novel sharp feature detection algorithm to select the feature points precisely. Finally, we update the point positions to fit them to the computed normals while retaining the sharp features that were detected. These steps are repeated until the noise is minimized. We evaluate our method using qualitative and quantitative comparisons with state-of-the-art denoising, normal estimation, and feature detection procedures. Our experiments show that our approach is competitive and, in most test cases, outperforms all other methods.

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References

  1. Alexa, M., Behr, J., Cohen-Or, D., et al.: Computing and rendering point set surfaces. IEEE Trans. Vis. Comput. Graph. 9(1), 3–15 (2003)

    Article  Google Scholar 

  2. Avron, H., Sharf, A., Greif, C., et al.: \(\ell _1\)-sparse reconstruction of sharp point set surfaces. ACM Transactions Graph. (TOG) 29(5), 1–12 (2010)

    Article  Google Scholar 

  3. Bazazian, D., Casas, J.R., Ruiz-Hidalgo, J.: Fast and robust edge extraction in unorganized point clouds. In: 2015 international conference on digital image computing: techniques and applications (DICTA), IEEE, pp 1–8 (2015)

  4. Béarzi, Y., Digne, J., Chaine, R.: Wavejets: A local frequency framework for shape details amplification. In: Computer Graphics Forum, Wiley Online Library, pp 13–24 (2018)

  5. Bellekens, B., Spruyt, V., Berkvens, R., et al.: A benchmark survey of rigid 3d point cloud registration algorithms. Int. J. Adv. Intell. Syst. 8, 118–127 (2015)

    Google Scholar 

  6. Ben-Shabat, Y., Lindenbaum, M., Fischer, A.: Nesti-net: Normal estimation for unstructured 3d point clouds using convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 10,112–10,120 (2019)

  7. Berger, M., Tagliasacchi, A., Seversky, L.M., et al.: A survey of surface reconstruction from point clouds. In: Computer Graphics Forum, Wiley Online Library, pp 301–329 (2017)

  8. Bernardini, F., Mittleman, J., Rushmeier, H., et al.: The ball-pivoting algorithm for surface reconstruction. IEEE Trans. Vis. Comput. Graph. 5(4), 349–359 (1999)

    Article  Google Scholar 

  9. Boulch, A., Marlet, R.: Deep learning for robust normal estimation in unstructured point clouds. In: Computer Graphics Forum, Wiley Online Library, pp 281–290 (2016)

  10. Buades, A., Coll, B., Morel, J.M.: A non-local algorithm for image denoising. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), IEEE, pp 60–65 (2005)

  11. Cao, C., Preda, M., Zaharia, T.: 3d point cloud compression: a survey. In: The 24th International Conference on 3D Web Technology, pp 1–9 (2019)

  12. Cazals, F., Pouget, M.: Estimating differential quantities using polynomial fitting of osculating jets. Comput. Aid. Geometric Des. 22(2), 121–146 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  13. Chen, H., Wei, M., Sun, Y., et al.: Multi-patch collaborative point cloud denoising via low-rank recovery with graph constraint. IEEE Trans. Vis. Comput. Graph. 26(11), 3255–3270 (2019)

    Article  Google Scholar 

  14. Cignoni, P., Callieri, M., Corsini, M., et al.: MeshLab: an open-source mesh processing tool. In: Scarano V, Chiara RD, Erra U (eds) Eurographics Italian Chapter Conference. The Eurographics Association, https://doi.org/10.2312/LocalChapterEvents/ItalChap/ItalianChapConf2008/129-136(2008)

  15. CPLEX IBM ILOG (2020) ILOG CPLEX Optimization Studio 20.1: User’s manual for CPLEX. https://www.ibm.com/products/ilog-cplex-optimization-studio

  16. Dabov, K., Foi, A., Katkovnik, V., et al.: Image denoising by sparse 3-d transform-domain collaborative filtering. IEEE Trans. Image Process. 16(8), 2080–2095 (2007)

    Article  MathSciNet  Google Scholar 

  17. Deschaud, J.E., Goulette, F.: Point cloud non local denoising using local surface descriptor similarity. IAPRS. 38(3A), 109–114 (2010)

    Google Scholar 

  18. Digne, J.: Similarity based filtering of point clouds. In: 2012 IEEE computer society conference on computer vision and pattern recognition workshops, IEEE, pp 73–79 (2012)

  19. Digne, J., Valette, S., Chaine, R.: Sparse geometric representation through local shape probing. IEEE Trans. Vis. Comput. Graph. 24(7), 2238–2250 (2017)

    Article  Google Scholar 

  20. Dinesh, C., Cheung, G., Bajić, I.V., et al.: Local 3d point cloud denoising via bipartite graph approximation & total variation. In: 2018 IEEE 20th International Workshop on Multimedia Signal Processing (MMSP), IEEE, pp 1–6 (2018)

  21. Dinesh, C., Cheung, G., Bajić, I.V.: Point cloud denoising via feature graph Laplacian regularization. IEEE Trans. Image Process. 29, 4143–4158 (2020)

    Article  MathSciNet  MATH  Google Scholar 

  22. Duan, C., Chen, S., Kovacevic, J.: 3d point cloud denoising via deep neural network based local surface estimation. In: ICASSP 2019–2019 IEEE International Conference on Acoustics, pp. 8553–8557. Speech and Signal Processing (ICASSP), IEEE (2019)

  23. Fleishman, S., Cohen-Or, D., Silva, C.T.: Robust moving least-squares fitting with sharp features. ACM Transactions Graph. (TOG) 24(3), 544–552 (2005)

    Article  Google Scholar 

  24. Foi, A., Dabov, K., Katkovnik, V., et al.: Shape-adaptive DCT for denoising and image reconstruction. In: Nasrabadi, N.M., Rizvi S.A., Dougherty E.R., Astola J.T., Egiazarian K.O. (eds.) Image Processing: Algorithms and Systems, Neural Networks, and Machine Learning. International Society for Optics and Photonics, vol. 6064, pp. 203–214 SPIE, (2006). https://doi.org/10.1117/12.64283

  25. Guennebaud, G., Gross, M.: Algebraic point set surfaces. In: ACM SIGGRAPH 2007 papers. p 23–es (2007)

  26. Guennebaud, G., Germann, M., Gross, M.: Dynamic sampling and rendering of algebraic point set surfaces. In: Computer Graphics Forum, Wiley Online Library, pp 653–662 (2008)

  27. Guerrero, P., Kleiman, Y., Ovsjanikov, M., et al.: Pcpnet learning local shape properties from raw point clouds. In: Computer Graphics Forum, Wiley Online Library, pp 75–85 (2018)

  28. Guillemot, T., Almansa, A., Boubekeur, T.: Non local point set surfaces. In: 2012 Second International Conference on 3D Imaging, Modeling, Processing, pp. 324–331. Visualization & Transmission, IEEE (2012)

  29. Guo, M., Song, Z., Han, C., et al.: Mesh denoising via adaptive consistent neighborhood. Sensors 21(2), 412 (2021)

    Article  Google Scholar 

  30. Han, X.F., Jin, J.S., Wang, M.J., et al.: A review of algorithms for filtering the 3d point cloud. Signal Process. Image Commun. 57, 103–112 (2017)

    Article  Google Scholar 

  31. Hermosilla, P., Ritschel, T., Ropinski, T.: Total denoising: Unsupervised learning of 3d point cloud cleaning. In: Proceedings of the IEEE International Conference on Computer Vision, pp 52–60 (2019)

  32. Hoppe, H., DeRose, T., Duchamp, T., et al.: Surface reconstruction from unorganized points. In: Proceedings of the 19th annual conference on computer graphics and interactive techniques, pp 71–78 (1992)

  33. Hu, G., Peng, Q., Forrest, A.R.: Mean shift denoising of point-sampled surfaces. Vis. Comput. 22(3), 147–157 (2006)

    Article  Google Scholar 

  34. Hu, W., Gao, X., Cheung, G., et al.: Feature graph learning for 3d point cloud denoising. IEEE Trans. Signal Process. 68, 2841–2856 (2020)

    Article  MathSciNet  MATH  Google Scholar 

  35. Huang, H., Li, D., Zhang, H., et al.: Consolidation of unorganized point clouds for surface reconstruction. ACM Transactions Graph. (TOG) 28(5), 1–7 (2009)

    Article  Google Scholar 

  36. Huang, H., Wu, S., Gong, M., et al.: Edge-aware point set resampling. ACM Transactions Graph. (TOG) 32(1), 1–12 (2013)

    Article  MATH  Google Scholar 

  37. Hurtado, J.: Detail-preserving mesh denoising using adaptive patches. Master’s thesis, Pontifícia Universidade Católica do Rio de Janeiro, Brazil (2018)

  38. Hurtado, J., Gattass, M., Raposo, A., et al.: Adaptive patches for mesh denoising. In: 2018 31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), pp 1–8 (2018)

  39. Kazhdan, M., Hoppe, H.: Screened Poisson surface reconstruction. ACM Transactions Graph. (ToG) 32(3), 1–13 (2013)

    Article  MATH  Google Scholar 

  40. Kivi, P.E., Mäkitalo, M.J., Žádník, J., et al. Real-time rendering of point clouds with photorealistic effects: a survey. IEEE Access 10:13,151–13,173 (2022)

  41. Leal, E., Sanchez-Torres, G., Branch, J.W.: Sparse regularization-based approach for point cloud denoising and sharp features enhancement. Sensors 20(11), 3206 (2020)

    Article  Google Scholar 

  42. Lipman, Y., Cohen-Or, D., Levin, D., et al.: Parameterization-free projection for geometry reconstruction. ACM Transactions Graph. (TOG) 26(3), 22 (2007)

    Article  Google Scholar 

  43. Liu, Z., Xiao, X., Zhong, S., Wang, W., Li, Y., Zhang, L., Xie, Z.: A feature-preserving framework 1504 for point cloud denoising. Comput. Aid. Des. 127, 102857 (2020). https://doi.org/10.1016/j.cad.2020.102857. https://www.sciencedirect.com/science/article/pii/S0010448520300506

  44. Lu, D., Lu, X., Sun, Y., Wang, J.: Deep feature-preserving normal estimation for point cloud filtering. Comput. Aid. Des. 125, 102860 (2020). https://doi.org/10.1016/j.cad.2020.102860. https://www.sciencedirect.com/science/article/pii/S0010448520300531

  45. Lu, X., Wu, S., Chen, H., et al.: Gpf: Gmm-inspired feature-preserving point set filtering. IEEE Trans. Vis. Comput. Graph. 24(8), 2315–2326 (2017)

    Google Scholar 

  46. Lu, X., Schaefer, S., Luo, J., et al.: Low rank matrix approximation for 3d geometry filtering. IEEE Transactions Vis. Comput. Graph. (2020b)

  47. Luo, S., Hu, W.: Differentiable manifold reconstruction for point cloud denoising. In: Proceedings of the 28th ACM International Conference on Multimedia, pp 1330–1338 (2020)

  48. Luo, S., Hu, W.: Score-based point cloud denoising. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp 4583–4592 (2021)

  49. Mattei, E., Castrodad, A.: Point cloud denoising via moving rpca. In: Computer Graphics Forum, Wiley Online Library, pp 123–137 (2017)

  50. Mederos, B., Velho, L., de Figueiredo, L.H.: Robust smoothing of noisy point clouds. In: Proc. SIAM Conference on Geometric Design and Computing, Citeseer, p 2 (2003)

  51. Mérigot, Q., Ovsjanikov, M., Guibas, L.J.: Voronoi-based curvature and feature estimation from point clouds. IEEE Trans. Vis. Comput. Graph. 17(6), 743–756 (2010)

    Article  Google Scholar 

  52. Nguyen, A., Le, B.: 3d point cloud segmentation: a survey. In: 2013 6th IEEE conference on robotics, automation and mechatronics (RAM), IEEE, pp 225–230 (2013)

  53. Öztireli, A.C., Guennebaud, G, Gross M.: Feature preserving point set surfaces based on non-linear kernel regression. In: Computer Graphics Forum, Wiley Online Library, pp 493–501 (2009)

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

    Article  MATH  Google Scholar 

  55. Rakotosaona, M.J., La Barbera, V, Guerrero P, et al (2020) Pointcleannet: Learning to denoise and remove outliers from dense point clouds. In: Computer Graphics Forum, Wiley Online Library, pp 185–203

  56. Rosman, G., Dubrovina, A., Kimmel, R.: Patch-collaborative spectral point-cloud denoising. In: Computer Graphics Forum, Wiley Online Library, pp 1–12 (2013)

  57. Roveri, R., Öztireli, A.C., Pandele, I., et al.: Pointpronets: Consolidation of point clouds with convolutional neural networks. In: Computer Graphics Forum, Wiley Online Library, pp 87–99 (2018)

  58. Roynard, X., Deschaud, J.E., Goulette, F.: Paris-lille-3d: a large and high-quality ground-truth urban point cloud dataset for automatic segmentation and classification. Int. J. Robotics Res. 37(6), 545–557 (2018)

    Article  Google Scholar 

  59. Sarkar, K., Bernard, F., Varanasi, K., et al.: Structured low-rank matrix factorization for point-cloud denoising. In: 2018 International Conference on 3D Vision (3DV), IEEE, pp 444–453 (2018)

  60. Schoenenberger, Y., Paratte, J., Vandergheynst, P.: Graph-based denoising for time-varying point clouds. In: 2015 3DTV-Conference: the true vision-capture, transmission and display of 3D video (3DTV-CON), IEEE, pp 1–4 (2015)

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

    Article  MATH  Google Scholar 

  62. The CGAL Project (2021) CGAL User and Reference Manual, 5.2.1 edn. CGAL Editorial Board, https://doc.cgal.org/5.2.1/Manual/packages.html

  63. Thompson, E.M., Biasotti, S., Giachetti, A., et al.: Shrec 2020: retrieval of digital surfaces with similar geometric reliefs. Comput. Graph. 91, 199–218 (2020)

    Article  Google Scholar 

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

    Article  Google Scholar 

  65. Wang, J., Xu, K., Liu, L., et al.: Consolidation of low-quality point clouds from outdoor scenes. In: Computer Graphics Forum, Wiley Online Library, pp 207–216 (2013)

  66. Wang, P.S., Liu, Y., Tong, X.: Mesh denoising via cascaded normal regression. ACM Trans. Graph. 35(6), 1–232 (2016)

    Google Scholar 

  67. Weber, C., Hahmann, S., Hagen, H., et al.: Sharp feature preserving mls surface reconstruction based on local feature line approximations. Graph. Models 74(6), 335–345 (2012)

    Article  Google Scholar 

  68. Wei, M., Chen, H., Zhang, Y., Xie, H., Guo, Y., Wang, J.: GeoDualCNN: Geometry supporting dual convolutional neural network for noisy point clouds. IEEE Transact. Vis. Comput. Graph. (2021). https://doi.org/10.1109/TVCG.2021.3113463

    Article  Google Scholar 

  69. Williams, R.M., Ilieş, H.T.: Practical shape analysis and segmentation methods for point cloud models. Comput. Aid. Geometric Des. 67, 97–120 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  70. Wu, S., Huang, H., Gong, M., et al.: Deep points consolidation. ACM Transactions Graph. (ToG) 34(6), 1–13 (2015)

    Article  Google Scholar 

  71. Yadav, S.K., Reitebuch, U., Skrodzki, M., et al.: Constraint-based point set denoising using normal voting tensor and restricted quadratic error metrics. Comput. Graph. 74, 234–243 (2018)

    Article  Google Scholar 

  72. Yu, L., Li, X., Fu, C.W., et al.: Ec-net: an edge-aware point set consolidation network. In: Proceedings of the European Conference on Computer Vision (ECCV), pp 386–402 (2018)

  73. Zeng, J., Cheung, G., Ng, M., et al.: 3d point cloud denoising using graph laplacian regularization of a low dimensional manifold model. IEEE Trans. Image Process. 29, 3474–3489 (2019)

    Article  MathSciNet  MATH  Google Scholar 

  74. Zhang, D., Lu, X., Qin, H., et al.: Pointfilter: point cloud filtering via encoder-decoder modeling. IEEE Trans. Visual Comput. Graph. 27(3), 2015–2027 (2020)

    Article  Google Scholar 

  75. Zhang, J., Cao, J., Liu, X., et al.: Multi-normal estimation via pair consistency voting. IEEE Trans. Vis. Comput. Graph. 25(4), 1693–1706 (2018)

    Article  Google Scholar 

  76. Zhang, W., Deng, B., Zhang, J., et al.: Guided mesh normal filtering. In: Computer Graphics Forum, Wiley Online Library, pp 23–34 (2015)

  77. Zheng, Y., Li, G., Wu, S., et al.: Guided point cloud denoising via sharp feature skeletons. Vis. Comput. 33(6–8), 857–867 (2017)

    Article  Google Scholar 

  78. Zheng, Y., Li, G., Xu, X., et al.: Rolling normal filtering for point clouds. Comput. Aid. Geometric Des. 62, 16–28 (2018)

    Article  MathSciNet  MATH  Google Scholar 

  79. Zhu L, Wei M, Yu J, et al (2013) Coarse-to-fine normal filtering for feature-preserving mesh denoising based on isotropic subneighborhoods. In: Computer Graphics Forum, Wiley Online Library, pp 371–380

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Acknowledgements

We would like to express our gratitude to the National Council for Scientific and Technological Development (CNPq) and the Tecgraf Institute (PUC-Rio) for their support.

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  1. Department of Informatics, Pontifical Catholic University of Rio de Janeiro, RJ, 22541-041, Rio de Janeiro, Brazil

    Jan Hurtado, Marcelo Gattass & Alberto Raposo

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  1. Jan Hurtado
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Hurtado, J., Gattass, M. & Raposo, A. 3D point cloud denoising using anisotropic neighborhoods and a novel sharp feature detection algorithm. Vis Comput 39, 5823–5848 (2023). https://doi.org/10.1007/s00371-022-02698-6

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