Pre-Processing and Surface Reconstruction of Points Cloud Based on Chord Angle Algorithm Technique
Although the rapid development in reverse engineering techniques, 3D laser scanners can be considered the modern technology used to digitize the 3D objects, but some troubles may be associate this process due to the environmental noises and limitation of the used scanners. So, in the present paper a data pre-processing algorithm has been proposed to obtain the necessary geometric features and mathematical representation of scanned object from its point cloud which obtained using 3D laser scanner (Matter and Form) through isolating the noised points. The proposed algorithm based on continuous calculations of chord angle between each adjacent pair of points in point cloud. A MATLAB program has been built to perform the proposed algorithm which implemented using a suggested case studies with cylinder and dome shape. The resulted point cloud from application the proposed algorithm and result of surface fitting for the case studies has been proved the proficiency of the proposed chord angle algorithm in pre-processing of data points and clean the point cloud, where the percent of data which was ignored as noisy data points according to proposed chord angle algorithm was arrived to (81.52%) and (75.01%)of total number of data points in point cloud for first and second case study respectively.
G. Wang, Y. Lv, N. Han, and D. Zhang, “Simplification Method and Application of 3D Laser Scan Point Cloud Data’’, International Conference on Mechanical Engineering and Material Science, MEMS, 2012.
J. Liu, J. Zhao, X. Yang, J. Liu, X. Qu, and X. Wang, “A Reconstruction Algorithm for Blade Surface Based on Less Measured Points”, Hindawi Publishing Corporation, IJAE, International Journal of Aerospace Engineering, Vol. 2015, Article ID 431824, 2015.
B. Cyganek, B. Krawczyk, and M. Woźniak, “Multidimensional Data Classification with Chordal Distance Based Kernel and Support Vector Machines”, Elsevier, EAAI, Engineering Applications of Artificial Intelligence, Vol. 46 pp. 10–22, 2015.
M. Xiao, Z. Qi, and H. Shi, “The Surface Flattening based on Mechanics Revision of the Tunnel 3D Point Cloud Data from Laser Scanner”, Elsevier, Procedia Computer Science, Vol. 131, pp. 1229–1237, 2018.
Z. Kang, L. Zhang , L. Tuo, B. Wang and J. Chen, “Continuous Extraction of Subway Tunnel Cross Sections Based on Terrestrial Point Clouds “, Remote Sensing journal, Vol. 2072-4292, pp. 857-879, 2014 .
F. Bernard et al, “Shape-aware Surface Reconstruction from Sparse 3D Point-Clouds”, https://github.com/fbernardpi/sparsePdmFitting 2016.
D. Xia et al, “Noise Properties of Chord-Image Reconstruction” IEEE Transactions on Medical Imaging, Vol. 26, No. 10, 2007.
S. Gauthier, W. Puech, R. Bénière, and G. Subsol, “Analysis of Digitized 3D Mesh Curvature Histograms for Reverse Engineering”, Elsevier, Computers in Industry, Vol. 82, pp. 67–83, 2017.
C. Mineo, S. G. Pierce, and R. Summan, “Novel Algorithms for 3D Surface Point Cloud Boundary Detection and Edge Reconstruction’’, CDE, journal of Computational Design and Engineering, Vol. 6, pp. 81–91, 2019.
K. W. Lee, and P. Bo, “Feature Curve Extraction from Point Clouds via Developable Strip Intersection”, Elsevier, journal of Computational Design and Engineering, Vol. 3, pp. 102–111, 2016.
A. Duroobi et al, “Reverse Engineering Representation Using an Image Processing Modification’’, Baghdad University Al-Khwarizmi Engineering Journal, vol. 15, pp. 56-62 , 2019.
D. A. Mahmood et al, “Improving Reverse Engineering Processes by using Articulated Arm Coordinate Measuring Machine’’, Baghdad University Al-Khwarizmi Engineering Journal, vol. 16, pps. 42-54 , 2020.
P. Comninos, “Mathematical and Computer Programming Techniques for Computer Graphics”, Springer, Verlag, London, 2006.
(Received 6 March 2020; accepted 5 July 2020)
Copyright: Open Access authors retain the copyrights of their papers, and all open access articles are distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution and reproduction in any medium, provided that the original work is properly cited. The use of general descriptive names, trade names, trademarks, and so forth in this publication, even if not specifically identified, does not imply that these names are not protected by the relevant laws and regulations. While the advice and information in this journal are believed to be true and accurate on the date of its going to press, neither the authors, the editors, nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein.