Combining Stereo and Fourier Transform Profilometry for 3D Scanning in Dynamic Environments

  • Maurício Edgar StivanelloEmail author
  • Marcelo Ricardo Stemmer


Fast 3D optical sensing of complex shapes remains challenging. While in the passive techniques, there is the correspondence problem, in the active techniques based on fringe projection, there is the challenge of phase recovery and disambiguation. We propose a stereo system that combines active and passive approaches in a complete 3D measurement system. A modified version of the Fourier transform profilometry technique is combined with the beating technique for absolute phase estimation. Stereo correspondence is performed using the obtained phase images. The results demonstrate that one or two frames are enough to estimate the 3D shape of complex objects, and therefore, the proposed approach can be applied for fast shape characterization.


3D measurement Fringe projection FTP Stereo matching Stereoscopy 



  1. Cherubini, A., Spindler, F., & Chaumette, F. (2014). Autonomous visual navigation and laser-based moving obstacle avoidance. IEEE Transactions on Intelligent Transportation Systems., 15(5), 2101–2110.CrossRefGoogle Scholar
  2. Facciolo, G., Limare, N., & Meinhardt-Llopis, E. (2014). Integral images for block matching. Image Processing On Line, 4, 344–369.CrossRefGoogle Scholar
  3. Gorthi, S. S., & Rastogi, P. (2010). Fringe projection techniques: Whither we are? Optics and Lasers in Engineering, 48(ARTICLE), 133–140.CrossRefGoogle Scholar
  4. Guo, X., Zhao, H., Jia, P., & Li, K. (2018). Multiview fringe matching profilometry in a projector–camera system. Optics Letters, 43(15), 3618–3621.CrossRefGoogle Scholar
  5. Henry, P., Krainin, M., Herbst, E., Ren, X., & Fox, D. (2010). Rgb-d mapping: Using depth cameras for dense 3d modeling of indoor environments. In In RGB-D: Advanced reasoning with depth cameras workshop in conjunction with RSS.Google Scholar
  6. Heshmat, S., Tomioka, S., & Nishiyama, S. (2014). Performance evaluation of phase unwrapping algorithms for noisy phase measurements. International Journal of Optomechatronics, 8(4), 260–274.CrossRefGoogle Scholar
  7. Jin, S., Cho, J., Pham, X. D., Lee, K. M., Park, S. K., Kim, M., et al. (2010). FPGA design and implementation of a real-time stereo vision system. IEEE Transactions on Circuits and Systems for Video Technology, 20(1), 15–26.CrossRefGoogle Scholar
  8. Junior, V. H., Stivanello, M. E., & Stemmer, M. R. (2017). Linear-time computation of indexing based stereo correspondence for cameras with automatic gain control. Journal of Signal Processing Systems, 90, 157–164.CrossRefGoogle Scholar
  9. Kawasaki, H., Furukawa, R., Sagawa, R., & Yagi, Y. (2008). Dynamic scene shape reconstruction using a single structured light pattern. In IEEE conference on computer vision and pattern recognition. CVPR.Google Scholar
  10. Lin, J. F., & Su, X. (1995). Two-dimensional fourier-transform profilometry for the automatic measurement of three-dimensional object shapes. Optical Engineering, 34, 3297–3303.CrossRefGoogle Scholar
  11. Newcombe, R. A., Izadi, S., Hilliges, O., Molyneaux, D., Kim, D., Davison, A. J., Kohli, P., Shotton, J., Hodges, S., & Fitzgibbon, A. (2010). Kinectfusion: Real-time dense surface mapping and tracking. In 10th IEEE international symposium on mixed and augmented reality (ISMAR).Google Scholar
  12. Ollikkala, A., & Makynen, A. (2009). Range imaging using a time-of-flight 3D camera and a cooperative object. In Proceedings of 12 MTC 2009—international instrumentation and measurement technology conference.Google Scholar
  13. Perez, L., Rodriguez, I., Rodriguez, N., Usamentiaga, R., & Garcia, D. (2016). Robot guidance using machine vision techniques in industrial environments: A comparative review. Sensors, 16(3), 335.CrossRefGoogle Scholar
  14. Pinto, T., Kohler, C., & Albertazzi, A. (2012). Regular mesh measurement of large free form surfaces using stereo vision and fringe projection. Optics and Lasers in Engineering, 50(7), 910–916.CrossRefGoogle Scholar
  15. Quan, C., Chen, W., & Tay, C. J. (2010). Phase-retrieval techniques in fringe-projection profilometry. Optics and Lasers in Engineering, 48(2), 235–243.CrossRefGoogle Scholar
  16. Reich, C., Ritter, R., & Thesing, J. (1997). White light heterodyne principle for 3D-measurement. In Proceedings - SPIE The International Society for Optical Engineering (vol. 3100, pp. 236–244).
  17. Scharstein, D., & Szeliski, R. (2002). A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. International Journal of Computer Vision, 47(1–3), 7–42.CrossRefzbMATHGoogle Scholar
  18. Siegwart, R., & Nourbakhsh, I. R. (2004). Introduction to autonomous mobile robots. Holland: Bradford Company.Google Scholar
  19. Stivanello, M. E., Leal, E. S., Palluat, N., & Stemmer, M. R. (2010). Dense correspondence with regional support for stereo vision systems. In SIBGRAPI conference on graphics, patterns and images.Google Scholar
  20. Takeda, M., Ina, H., & Kobayashi, S. (1982). Fourier-transform method of fringe-pattern analysis for computer-based topography and interferometry. Journal of the Optical Society of America, 72(1), 156–160.CrossRefGoogle Scholar
  21. Takeda, M., Gu, Q., Kinoshita, M., Takai, H., & Takahashi, Y. (1997). Frequency-multiplex fourier-transform profilometry: A single-shot three-dimensional shape measurement of objects with large height discontinuities and/or surface isolations. Applied Optics Communications, 36(22), 5347–5354.CrossRefGoogle Scholar
  22. Takeda, M., & Mutoh, K. (1983). Fourier transform profilometry for the automatic measurement of 3-D object shapes. Applied Optics, 22(24), 3977–3982.CrossRefGoogle Scholar
  23. Trucco, E., & Verri, A. (1998). Introductory techniques for 3-D computer vision. Upper Saddle River: Prentice Hall.Google Scholar
  24. Weise, T., Leibe, B., & Gool, L. V. (2007). Fast 3D scanning with automatic motion compensation. In IEEE conference on computer vision and pattern recognition.Google Scholar
  25. Yamaguchi, I., & Zhang, T. (1997). Phase-shifting digital holography. Optics Letters, 22(16), 1268–1270.CrossRefGoogle Scholar
  26. Yong, L., Yuan, J., Yong, J., & Luo, X. (2017). 3D measurement of large-scale object using independent sensors. In Three-dimensional imaging, visualization, and display 2017.Google Scholar
  27. Yoshizawa, T. (2015). Handbook of optical metrology: Principles and applications. Boca Raton: CRC Press.Google Scholar
  28. Zhang, S. (2018). High-speed 3D shape measurement with structured light methods: A review. Optics and Lasers in Engineering, 106, 119–131.CrossRefGoogle Scholar
  29. Zhang, Z. (2000). A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(11), 1330–1334.
  30. Zhang, Z., & Zhang, S. (2009). One-shot 3D shape and color measurement using composite RGB fringe projection and optimum three-frequency selection. In 2009 international conference on optical instruments and technology.Google Scholar
  31. Zumbrunn, R. (1987). Automated fast shape determination of diffuse reflecting objects at close range by means of structured light and digital phase measurement. In ISPRS Intermission Conference on Fast Processing of Photogrammetric Data (pp. 363–379).Google Scholar

Copyright information

© Brazilian Society for Automatics--SBA 2019

Authors and Affiliations

  1. 1.Federal Institute of Santa CatarinaFlorianópolisBrazil
  2. 2.Federal University of Santa CatarinaFlorianópolisBrazil

Personalised recommendations