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Point cloud segmentation for complex microsurfaces based on feature line fitting

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Abstract

Surfaces based on feature line constraints have higher accuracy than free-form surfaces and can capture other geometric relations of the model. The parts of complex microsurfaces are formed by arrays and crossings of several small surfaces. Many problems can be encountered in identifying feature points and fitting feature lines, which are difficult to solve by reverse engineering. In this study, feature point extraction, feature line fitting, and three-dimensional segmentation were investigated. First, the connection between two surfaces and the corresponding differential geometric quantities were explored. Then, a feature point extraction method for complex models was proposed. Second, the problems of separation, simplification, and combination of feature points for different models were analyzed, and the feature lines used to segment the point cloud were constructed. Finally, a region growth method based on feature line constraints was proposed to segment the point cloud data. Experimental results show that this method can solve the problem of excessive and insufficient segmentation for complex microsurface point cloud data and thus represents a foundation for high-quality model reconstruction.

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References

  1. Abualigah LM, Khader AT, Hanandeh ES (2018) Hybrid clustering analysis using improved krill herd algorithm[J]. Appl Intell 48(11):4047–4071

    Article  Google Scholar 

  2. Altantsetseg E, Muraki Y, Matsuyama K, Konno K (2013) Featurel line extraction from unorgnized noisy point clouds using truncated fourier series[J]. Vis Comput 29(6):617–626

    Article  Google Scholar 

  3. An Y, Zhao P, Li Z et al (2016) Self-adaptive polygon mesh reconstruction based on ball-pivoting algorithm[J]. Int J Comput Appl Technol 54(1):51–60

    Article  Google Scholar 

  4. Angelina S, Suresh LP, Veni SHK (2012) Image segmentation based on genetic algorithm for region growth and region merging. International Conference on Computing Electronics and Electrical Technologies (ICCEET), March 21–22, 2012, Kumaracoil, India, 970-974

  5. Angelo LD, Stefano PD (2015) Geometric segmentation of 3D scanned surfaces[J]. Computer-Aided Design 62:44–56

    Article  Google Scholar 

  6. Angelo LD, Stefano PD, Giaccari L (2011) A new mesh-growing algorithm for fast surface reconstruction[J]. Comput -Aid Des 43:639–650

    Article  Google Scholar 

  7. Anwer N, Mathieu L (2016) From reverse engineering to shape engineering in mechanical design[J]. Annals CIRP 65:165–168

    Article  Google Scholar 

  8. Chen XY, Yang Y, Xiang YF (2017) Measurement of Point Cloud Data Segmentation Based on Euclidean Clustering Algorithm[J]. Bull Surv Map 11:27–31

    Google Scholar 

  9. Chen HW, Yuan XC, Wu LS et al (2019) Automatic point cloud feature-line extraction algorithm based on curvature-mutation analysis[J]. Optics Precision Eng 27(05):1218–1228

    Article  Google Scholar 

  10. Daniels J, Ha LK, Ochotta T, Silva CT (2007) Robust smooth feature extraction from point clouds, vol 123. IEEE International Conference on Shape Modeling and Applications 2007 (SMI '07), Lyon

    Google Scholar 

  11. Demarsin K, Vanderstraeten D, Volodine T et al (2017) Detection of closed sharp edges in point clouds using normal estimation and graph theory[J]. Comput Aided Design 39(4):276–283

    Article  Google Scholar 

  12. Di AL, Di SP, Spezzaneve A (2012) A method for 3D detection of symmetry line in asymmetric postures[J]. Comput Methods Biomech Biomed Eng 16:1213–1220

    Google Scholar 

  13. Gao R, Li SG, Huang X et al (2017) Extraction of Feature Point From Scattered Points in Complex Curved Part[J]. Aeronautical Manuf Technol 60(13):60–65

    Google Scholar 

  14. Hackel T, Wegner JD, Schindler K (2016) fast semantic segmentation of 3d point clouds with strongly varying density. J. ISPRS Ann. Photogrammetry, remote Sens. Spatial Inf Sci III-3:177–184

    Google Scholar 

  15. Hackel T, Wegner J D, Schindler K (2016) Contour Detection in Unstructured 3D Point Clouds. 2016 IEEE Conference on computer vision and pattern recognition (CVPR), Las Vegas, pp. 1610–1618

  16. He T, Xiong FG, Han X et al (2018) A Feature Curve Extraction Algorithm for Point Cloud Based on Covariance Matrix[J]. Comput Eng 44(03):275–280+286

    Google Scholar 

  17. Hu SL, Zhou MQ, Shui WY et al (2015) Improved pivoting ball algorithm for nonuniform point cloud data[J]. J Syst Simul 27(10):2446–2452

    Google Scholar 

  18. Huang GZ (2015) Study on algorithm of data segmentation for Discerete point cloud data in reverse engineering Zhenjiang: Jiangsu University

  19. Kazhdan M, Hoppe H (2013) Screened poisson surface reconstruction[J]. ACM Trans Graphics 32(3):1–13

    Article  Google Scholar 

  20. Lawin FJ, Danelljan M, Tosteberg P, Bhat G, Khan FS, Felsberg M (2017) Deep Projective 3D Semantic Segmentation[J]. Lecture Note Comput Sci 10424:95–107

    Article  MathSciNet  Google Scholar 

  21. Li RZ, Liu YY, Yang M, Zhang HH (2018) 3D point cloud segmentation based on improved region growth[J]. Las Optoelect Prog 55:325–331

    Google Scholar 

  22. Lu L, Zhang J, Gao C et al (2011) Two-directional two-dimensional random projection and its variations for face and Palmprint recognition[M]// computational science and its applications - ICCSA 2011. Springer, Berlin

    Google Scholar 

  23. Ni TG, Ma ZH (2010) A fast surface reconstruction algorithm for 3D unorganized points[C]// international conference on computer engineering and technology, Chengdu, 15–18

  24. Ni H, Liu XG, Ning XG et al (2016) Edge detection and feature line tracing in 3D-point clouds by analyzing geometric properties of neighborhoods[J]. Remote Sens 8(9):710

    Article  Google Scholar 

  25. Nie JH, Liu Y, Gao H, Wang BY et al (2015) Detection from Point Cloud Based on signed Surface Variation and Region Segmentation[J]. Comput-Aid Desig Comput Graph 27(12):98–105

    Google Scholar 

  26. Vaswani N, Narayanamurthy P (2018) Static and dynamic robust pcavia low-rank + sparse matrix decomposition: A review submitted to Proceedings of the IEEE Special Issue on Rethinking PCA for Modern Datasets

  27. Vo A-V, Truong-Hong L, Laefer DF, Bertolotto M (2015) Octree-based region growing for point cloud segmentation[J]. ISPRS J Photogram Remote Sens 104(88–100):88–100

    Article  Google Scholar 

  28. Wang XC, Liu XP, Li BJ, Zhang SG (2013) Feature Detection on Point Cloud via Local Reconstruction[J]. Comput-Aid Desig Comput Graph 25:659–665

    Google Scholar 

  29. Wang XH, Chen HW, Wu LS (2020) Feature extraction of point clouds based on region clustering segmentation. Multimed Tools Appl 79:11861–11889

    Article  Google Scholar 

  30. Weber C, Hahmann S, Hagen H (2010) Sharp feature detection in point clouds. In: Shape Modeling International Conference(SMI), Aix-en-Provence:IEEE, pp. 175–186

  31. Xia S, Wang R (2017) A fast edge extraction method for Mobile Lidar point clouds[J]. IEEE Geosci Remote Sens Lett 14:1288–1292

    Article  Google Scholar 

  32. Xie XR, Ge SF (2014) Feature extraction algorithm for point cloud model based on MLS[J]. J Shenyang Univ Technol 36(3):308–315

    Google Scholar 

  33. Zhang YH, Geng GH, Wei XR et al (2016) A statistical approach for extraction of feature lines from point clouds [J]. Comput Graphics 56:31–45

    Article  Google Scholar 

  34. Zhao H, Anwer N, Bourdet P (2013) Curvature-based registration and segmentation for multisensor coordinate metrology. J Procedia CIRP 10:112–118

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by “the National Natural Science Foundation of China” under Grant No. 51105175 and “Six Talent Climax Foundation of Jiangsu Province” under Grant No. JXQC-006.

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Authors and Affiliations

  1. School of Mechanical Engineering, Jiangnan University, Wuxi, 214122, Jiangsu, China

    Xiaogang Ji, Xixi Zhang & Haitao Hu

  2. Jiangsu Key Laboratory of Advanced Food Manufacturing Equipment and Technology (Jiangnan University), Wuxi, 214122, Jiangsu, China

    Xiaogang Ji

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  1. Xiaogang Ji
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  2. Xixi Zhang
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  3. Haitao Hu
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Correspondence to Xiaogang Ji.

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Ji, X., Zhang, X. & Hu, H. Point cloud segmentation for complex microsurfaces based on feature line fitting. Multimed Tools Appl 80, 4553–4578 (2021). https://doi.org/10.1007/s11042-020-09910-6

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