Open Access
Issue
MATEC Web Conf.
Volume 71, 2016
The International Conference on Computing and Precision Engineering (ICCPE 2015)
Article Number 04008
Number of page(s) 9
Section Advanced Manufacturing and Analysis Technology
DOI https://doi.org/10.1051/matecconf/20167104008
Published online 02 August 2016
  1. T. Bodenmueller and G. Hirzinger, “Online Surface Reconstruction from Unorganized 3-D-Points for the DLR Hand-guided Scanner System,” Proceedings of 2nd International Symposium on 3-D Data Processing, Visualization and Transmission, 285–292 (2004). [CrossRef] [Google Scholar]
  2. K. H. Strobl, W. Sepp, E. Wahl, T. Bodenmueller, M. Suppa, J. Seara, and G. Hirzinger, “The DLR Multisensory Hand-Guided Device: The Laser Stripe Profiler,” In Proceedings of ICRA, 2 1927–1932 (2004). [Google Scholar]
  3. A. W. Fitzgibbon, G. Cross, and A. Zisserman, “Automatic 3-D model construction for turn-table sequences,” In 3-D Structure from Multiple Images of Large-Scale Environments, 155–170 (1998) [Google Scholar]
  4. V. Fremont and R. Chellali, “Turntable-based 3-D object reconstruction,” In IEEE Conference on Cybernetics and Intelligent Systems, 1277–1282 (2004) [Google Scholar]
  5. M. Callieri, A. Fasano, G. Impoco, P. Cignoni, R. Scopigno, G. Parrini, and G. Biagini, “RoboScan: An Automatic System for Accurate and Unattended 3-D Scanning,” In IEEE 3-DPVT, Thessaloniki, Greece, Sept. 805–812 (2004). [Google Scholar]
  6. S. Larsson and J. A. P. Kjellander, “Path planning for laser scanning with an industrial robot,” In RAS, 7, 615–624, (2008). [Google Scholar]
  7. Simon Kriegel, Christian Rink, Tim Bodenmüller and Michael Suppa. “Efficient Next-Best-Scan Planning for Autonomous 3-D Surface Reconstruction of Unknown Objects”, Journal of Real Time Image Processing: Special Issue on Robot Vision, 1–21 (2013). [Google Scholar]
  8. P. J. Besl and N. D. McKay, “A method for registration of 3-D shapes,” IEEE Transactions on Pattern Recognition and Machine Intelligence 14, 239–256 (1992). [Google Scholar]
  9. C. J. R. Chua, “Point signatures: a new representation for 3-D object recognition,” Int. J. Comput. Vision 25 63–85 (1997) [CrossRef] [Google Scholar]
  10. A. E. Johnson and M. Hebert, “Using spin images for efficient object recognition in cluttered 3-D scenes,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 21 433–449 (1999). [CrossRef] [Google Scholar]
  11. F. Tombari, S. Salti, and L. Di Stefano, “Unique Signatures of Histograms for Local Surface Description,” In Proceedings of the 11th European Conference on Computer Vision Conference on Computer Vision: Part III, 356–369 (2010) [Google Scholar]
  12. Liang-Chia Chen, Dinh-Cuong Hoang, Hsien-I Lin, Thanh-Hung Nguyen, “An Automated Point Clouds Scanning and Registration Methodology for 3-D Object Reconstruction,” In The 39th National Conference on Theoretical and Applied Mechanics, (2015). [Google Scholar]
  13. L.-C. Chen, T.-H. Nguyen, and S.-T. Lin, “3-D Object Recognition and Localization for Robot Pick and Place Application Employing a Global Area Based Descriptor,” In International conference on Advanced Robotics and Intelligent Systems, Taipei, Taiwan, May 29-31 (2015). [Google Scholar]
  14. S. Gottschalk, M. C. Lin, and D. Manocha, “OBBTree: A Hierarchical Structure for Rapid Interference Detection,” In Proceedings of the 23rd Annual Conference on Computer Graphics and Interactive Techniques, 171–180 (1996). [Google Scholar]
  15. Z. C. Marton, R. B. Rusu, and M. Beetz, “On fast surface reconstruction methods for large and noisy point clouds,” In IEEE International Conference on Robotics and Automation, 3218–3223 (2009). [Google Scholar]
  16. M. A. Fischler and R. C. Bolles, “Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography,” Commun. ACM, 24 381–395 (1981). [Google Scholar]
  17. T. H. Nguyen, “3-D Object Recognition and Localization of Randomly Stacked Objects for Automation”. Doctoral Thesis, National Taipei University of Technology, Taiwan, (2015). [Google Scholar]

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