View More View Less
  • 1 Department of Surveying, Faculty of Civil Engineering Slovak University of Technology in Bratislava, Radlinského 11, 810 05, Bratislava
Restricted access

Purchase article

USD  $25.00

Purchase this article

USD  $387.00

Nowadays huge datasets can be collected in a relatively short time. After capturing these data sets the next step is their processing. Automation of the processing steps can contribute to efficiency increase, to reduction of the time needed for processing, and to reduction of interactions of the user. The paper brings a short review of the most reliable methods for sphere segmentation. An innovative algorithm for automated detection of spheres and for estimating their parameters from 3D point clouds is introduced. The algorithm proposed was tested on complex point clouds. In the last part of the paper, the implementation of the algorithm proposed to a standalone application is described.

  • [1]

    Schnabel R., Wahl R., Klein R. Efficient RANSAC for point-cloud shape detection, Computer Graphics Forum, Vol. 26, No. 2, 2007, pp. 214226.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [2]

    Honti R., Erdélyi J., Kopáčik A. Plane segmentation from point clouds, Pollack Periodica, Vol. 13, No. 2, 2018, pp. 159171.

  • [3]

    Tran T. T., Cao V. T., Laurendeau D. Extraction of cylinders and estimation of their parameters from point clouds, Computers & Graphics, Vol. 46, 2015, pp. 345357.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [4]

    Honti R., Erdélyi J., Kopáčik A. Automation of cylinder segmentation from point cloud data, Pollack Periodica, Vol. 14, No. 3, 2019, pp. 189200.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [5]

    Várady T., Martin R. R., Cox J. Reverse engineering of geometric models-an introduction, Computer-Aided Design, Vol. 29, No. 4, 1997, pp. 255268.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [6]

    Benkő P., Várady T. Segmentation methods for smooth point regions of conventional engineering objects, Computer-Aided Design, Vol. 36, No. 6, 2004, pp. 511523.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [7]

    van der Glas M., Vos F. M., Botha C. P., Vossepoel A. M. Determination of position and radius of ball joints, Proceedings SPIE 4684, Medical Imaging, Image Processing, San Diego, California, United States, 9 May 2002, pp. 15711575.

    • Search Google Scholar
    • Export Citation
  • [8]

    Wang L., Cao J., Han C. A calibration algorithm for 3D laser scanner based on spatial sphere, Journal of Xi'an Jiaotong University, Vol. 47, No. 4, 2013, pp. 7985.

    • Search Google Scholar
    • Export Citation
  • [9]

    Agrawal M., Davis L. S. Camera calibration using spheres: a semi-definite programming approach, Proceedings of the Ninth IEEE International Conference on Computer Vision, Nice, France, 13-16 October 2003, Vol. 2, pp. 782789.

    • Search Google Scholar
    • Export Citation
  • [10]

    Wang Y., Shi H., Zhang Y., Zhang D. Automatic registration of laser point cloud using precisely located sphere targets, Journal of Applied Remote Sensing, Vol. 8, No. 1, 2014, Paper No. 083588.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [11]

    Huang J., Wang Z., Gao J., Huang Y., Towers D. P. High-precision registration of point clouds based on sphere feature constraints, Sensors, Vol. 17, No. 1, 2017, pages 114.

    • Search Google Scholar
    • Export Citation
  • [12]

    Camurri M., Vezzani R., Cucchiara R. 3D Hough transform for sphere recognition on point clouds, Machine Vision and Applications, Vol. 25, No. 7, 2014, pp. 18771891.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [13]

    Ogundana T., Coggrave C. R., Burguete R., Huntley J. M. Fast Hough transform for automated detection of spheres in three-dimensional point clouds, Optical Engineering, Vol. 46, No. 5, 2007, Paper No. 051002.

    • Search Google Scholar
    • Export Citation
  • [14]

    Fischler M. A., Bolles R. C. Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography, Communications of the ACM, Vol. 24, No. 6, 1981, pp. 381395.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [15]

    Duda R. O., Hart P. E. Use of the Hough transformation to detect line and curves in pictures, Communications of the ACM, Vol. 15, No. 1, 1972, pp. 1115.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [16]

    Abuzaina A., Nixon M. S., Carter J. N. Sphere detection in kinect point clouds via the 3D Hough transform, in: Wilson R., Hancock E., Bors A., Smith W. (Eds) Computer Analysis of Images and Patterns, ser. Lecture Notes in Computer Science, Vol. 8048, Springer, 2013, pp. 290297.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [17]

    Tran T. T., Cao V. T., Laurendeau D. eSphere: extracting spheres from unorganized point clouds, The Visual Computer, Vol. 32, No. 10, 2016, pp. 12051222.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • [18]

    Pratt V. Direct least-squares fitting of algebraic surfaces, ACM SIGGRaph Computer Graphics, Vol. 21, No. 4, 1987, pp. 145152.

  • [19]

    Forbes A. B. Least-squares best-fit geometric elements, National Physical Laboratory, Report, Vol. 140, 1989, London, UK, pages 130.

  • [20]

    Jekel C. F. Digital image correlation on steel ball (Appendix A),. in Obtaining non-linear orthotropic material, (Diploma Thesis) Stellenbosch, South Africa, Stellenbosch University, 2016.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 263 263 11
Full Text Views 35 35 0
PDF Downloads 22 22 0