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User-friendly 3D Object Reconstruction Method based on Structured Light in Ubiquitous Environments

유비쿼터스 환경에서 구조광 기반 사용자 친화적 3차원 객체 재구성 기법

  • Received : 2013.08.16
  • Accepted : 2013.11.13
  • Published : 2013.11.28

Abstract

Since conventional methods for the reconstruction of 3D objects used a number of cameras or pictures, they required specific hardwares or they were sensitive to the photography environment with a lot of processing time. In this paper, we propose a 3D object reconstruction method using one photograph based on structured light in ubiquitous environments. We use color pattern of the conventional method for structured light. In this paper, we propose a novel pipeline consisting of various image processing techniques for line pattern extraction and matching, which are very important for the performance of the object reconstruction. And we propose the optimal cost function for the pattern matching. Using our method, it is possible to reconstruct a 3D object with efficient computation and easy setting in ubiquitous or mobile environments, for example, a smartphone with a subminiature projector like Galaxy Beam.

Keywords

Ubiquitous Environment;3D Data Reconstruction;Structured Light;Image Processing;DLP Projector

References

  1. T. Acharya and A. Ray, Image Processing - Principles and Applications, Wiley InterScience, 2006.
  2. D. Scharstein and R. Szeliski, "A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms," International Journal of Computer Vision, Vol.47, No.1, pp.7-42, 2002. https://doi.org/10.1023/A:1014573219977
  3. J. Turner, M. L. Braunstein, and G. J. Andersen, "Relationship between Binocular Disparity and Motion Parallax in Surface Detection," Percept Psychophys, Vol.59, No.3, pp.370-380, 1997. https://doi.org/10.3758/BF03211904
  4. J. Posamer and M. Altschuler, "Surface Measurement by Space-Encoded Projected Beam System," Computer Graphics and Image Processing, Vol.18, No.1, pp.1-17, 1982. https://doi.org/10.1016/0146-664X(82)90096-X
  5. D. Capsi, N. Kiryati, and J. Shamir, "Range Imaging with Adaptive Color Structured Light," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol.20, No.5, pp.470-480, 1998. https://doi.org/10.1109/34.682177
  6. J. Pages, J. Salvi, R. Gracia, and C. Matabosch, "Overview of Coded Light Projection Techniques for Automatic 3D Profiling," IEEE Int'l Conf. on Robotics and Automation, pp.133-138, 2003.
  7. D. Lanman and G. Taubin, Build Your Own 3D Scanner: Optical Triangulation for Beginners, Courses on Siggraph, 2009.
  8. J. Beraldin, F. Blais, L. Cournoyer, G. Godin, and M. Rioudx, "Active 3D Sensing," NRC Technical Report, 2000.
  9. S. Ryusuke, "Dense 3D Reconstruction Method Using a Single Pattern for Fast Moving Object," IEEE Computer Vision 12th Int'l Conf., pp.1779-1786, 2009.
  10. H. Fredricksen, "The Lexicographically Least De Brujin Cycle," Journal of Combinatorial Theory, Vol.9, No.1, pp.509-510, 1970.
  11. J. Geng, "Structured-light 3D surface imaging: a tutorial," Advances in Optics and Photonics, Vol.3, No.2, pp.128-160, 2011. https://doi.org/10.1364/AOP.3.000128
  12. U. Kthe, "Edge and Junction Detection with an Improved Structure Tensor," Pattern Recogniton Proc. of 25th DAGM Symposium, pp.25-32, 2003.
  13. Roger Y. Tsai, "An Efficient and Accurate Camera Calibration Technique for 3D Machine Vision," Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, pp.364-374, 1986.
  14. 서정구, 강의선, "FOV 모델과 2D 패턴을 이용한 왜곡 중심 추정 기법", 한국콘텐츠학회논문지, 제13권, 제8호, pp.11-19, 2013. https://doi.org/10.5392/JKCA.2013.13.08.011
  15. R. Hartley, Multiple View Geometry in Computer Vision, Cambridge Univ Press, pp.155-157.
  16. http://www.trimensional.com/

Cited by

  1. 3D Accuracy Analysis of Mobile Phone-based Stereo Images vol.19, pp.5, 2014, https://doi.org/10.5909/JBE.2014.19.5.677

Acknowledgement

Supported by : 한국산업기술평가관리원, 한국연구재단