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Algorithm for Point Cloud Compression based on Geometrical Features

Volume 15, Number 3, March 2019, pp. 782-791
DOI: 10.23940/ijpe.19.03.p7.782791

Shiquan Qiao, Kun Zhang, and Kai Gao

School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang, 050018, China 


(Submitted on October 23, 2018; Revised on November 24, 2018; Accepted on December 27, 2018)

Abstract:

As a common and important form, point cloud data exists in computer graphics, especially for 3D visualization. However, with the development of 3D scanning technology, huge data sets have become a main burden in the data processing of point clouds. Therefore, the technology of point cloud compressing is a key content in data pre-processing. This paper provides a new algorithm to compress the point cloud data set. The compressing algorithm can be carried out based on the feature of measure objects. In order to find the data feature, we firstly introduce a point cloud compressing model based on conicoid according to the measure objects. Secondly, for the comparison of the features between the model and the point cloud, we provide a shape operator and a contour operator based on the estimation of geometrical features. Then, according to the value of the shape operator and the contour operator, we provide a matching model. The compressing data algorithm can be created through the matching computation of geometrical features. At last, we use the experiment to prove the feasibility of compressing algorithm, and compare the result of the proposed algorithm and the result of other algorithms in terms of the running time and the compressing effect.

 

References: 20

    1. S. Song, J. Liu, and C. Yin, “Data Reduction for Point Cloud using Octree Coding,” in Proceedings of International Conference on Intelligent Computing, pp. 376-383, Springer, 2017
    2. R. Schnabel and R. Klein, “Octree-based Point-Cloud Compression,” in Proceedings of Eurographics Symposium on Point-Based Graphics, pp. 111-120, 2006
    3. Q. Xie and X. Xie, “Point Cloud Data Reduction Methods of Octree-based Coding and Neighborhood Search,” in Proceedings of 2011 International Conference on Electronic and Mechanical Engineering and Information Technology (EMEIT), pp. 3800-3803, 2011
    4. Y. Wang, J. Tang, Q. Rao, and C. Yuan, “High-Efficient Point Cloud Simplification based on the Improved K-Means Clustering Algorithm,” Journal of Computational Information Systems, Vol. 11, No. 2, pp. 433-440, 2015
    5.  H. Benhabiles, O. Aubreton, H. Barki, and H. Tabia, “Fast Simplification with Sharp Feature Preserving for 3D Point Clouds,” in Proceedings of International Symposium on Programming and Systems, pp. 47-52, 2013
    6. B. Q. Shi, J. Liang, and Q. Liu, “Adaptive Simplification of Point Cloud using K-Means Clustering,” Computer-Aided Design, Vol. 43, No. 8, pp. 910-922, 2011
    7. T. Li, Q. Pan, L. Gao, and P. Li, “A Novel Simplification Method of Point Cloud with Directed Hausdorff Distance,” in Proceedings of IEEE International Conference on Computer Supported Cooperative Work in Design, pp. 469-474, 2017
    8. X. C. Yuan, W. U. Lu-Shen, and H. W. Chen, “Feature Preserving Point Cloud Simplification,” Optics & Precision Engineering, Vol. 23, No. 9, pp. 2666-2676, 2015
    9. W. Zhang and J. Kosecka, “Image based Localization in Urban Environments,” in Proceedings of International Symposium on 3d Data Processing, Visualization, and Transmission, pp. 33-40, 2006
    10. K. Ni, A. Kannan, A. Criminisi, and J. Winn, “Epitomic Location Recognition,” IEEE Transactions on Pattern Analysis & Machine Intelligence, Vol. 31, No. 12, pp. 2158, 2009
    11. E. Kalogerakis, O. Vesselova, J. Hays, and A. A. Efros, “Image Sequence Geolocation with Human Travel Priors,” in Proceedings of IEEE International Conference on Computer Vision, pp. 253-260, 2010
    12. Houshiar, D. Borrmann, J. Elseberg, and A. Nüchter, “Panorama based Point Cloud Reduction and Registration,” in Proceedings of International Conference on Advanced Robotics, pp. 1-8, 2013
    13. P. Gospodarczyk, “Degree Reduction of Bézier Curves with Restricted Control Points Area,” in Proceedings of International Conference on Computer Supported Cooperative Work in Design, pp. 649-656, 2011
    14. V. Morell, S. Orts, M. Cazorla, and J. Garcia-Rodriguez, “Geometric 3D Point Cloud Compression,” Pattern Recognition Letters, Vol. 50, No. C, pp. 55-62, 2014
    15. T. Whelan, L. Ma, E. Bondarev, P. H. N. D. With, and J. Mcdonald, “Incremental and Batch Planar Simplification of Dense Point Cloud Maps,” Robotics & Autonomous Systems, Vol. 69, No. C, pp. 3-14, 2015
    16. G. Shi, X. Dang, and X. Gao, “Research on Adaptive Point Cloud Simplification and Compression Technology based on Curvature estimation of Energy Function,” Revista de la Facultad de Ingeniería UCV, Vol. 32, pp. 336-343, 2017
    17. H. S. Park, Y. Wang, E. Nurvitadhi, J. C. Hoe, Y. Sheikh, and M. Chen, “3D Point Cloud Reduction using Mixed-Integer Quadratic Programming,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 229-236, 2013
    18. H. Houshiar and A. Nüchter, “3D Point Cloud Compression using Conventional Image Compression for Efficient Data Transmission,” in Proceedings of 2015 XXV International Conference on Information, Communication and Automation Technologies (ICAT), pp. 1-8, IEEE, 2015
    19. H. Kim, B. Liu, C. Y. Goh, S. Lee, and H. Myung, “Robust Vehicle Localization using Entropy-Weighted Particle Filter-based Data Fusion of Vertical and Road Intensity Information for a Large Scale Urban Area,” IEEE Robotics and Automation Letters, Vol. 2, No. 3, pp. 1518-1524, 2017
    20. X. Zhang, W. Wan, and X. An, “Clustering and DCT based Color Point Cloud Compression,” Journal of Signal Processing Systems, Vol. 86, No. 1, pp. 41-49, 2017

       

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