Skip to main content

Abstract

Consumer depth cameras belong to two technological families; structured light depth cameras employ active triangulation, while matricial Time-of-Flight cameras operate on the basis of the Time-of-Flight (ToF) principle. We introduce foundational concepts for understanding both families and cover the computer vision concepts behind pin-hole imaging, camera calibration, 2-view, and N-view stereo which lie at the heart of operating structured light cameras. The chapter also introduces the Time-of-Flight (ToF) principle and modulation types used in point-wise ToF measurements, as well as operations of the matricial ToF sensors which ToF depth cameras rely on.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 199.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    It is worth to notice that the phase \(\varphi _{c}\) of the carrier at the transmitter side is generally different from the phase \(\varphi _{c}^{{\prime}}\) at the receiver. Both \(\varphi _{c}\) and \(\varphi _{c}^{{\prime}}\) are usually unknown, especially in the case of a non-coherent process which is the typical practical solution. However, the system does not need to be aware of the values of \(\varphi _{c}\) and \(\varphi _{c}^{{\prime}}\) and it is inherently robust to the lack of their knowledge.

References

  1. Faro, http://faro.com Accessed March 2016

  2. Iee, http://www.iee.lu Accessed March 2016

  3. Intel RealSense, www.intel.com/realsense Accessed March 2016

  4. Leica, http://hds.leica-geosystems.com Accessed March 2016

  5. Mesa imaging, http://www.mesa-imaging.ch Accessed March 2016

  6. OpenCV, http://opencv.org Accessed March 2016

  7. Pmd technologies, http://www.pmdtec.com/ Accessed March 2016

  8. Riegl, http://www.riegl.com/ Accessed March 2016

  9. Velodyne lidar, http://www.velodynelidar.com Accessed March 2016

  10. Zoller and Frolich, http://www.zf-laser.com/ Accessed March 2016

  11. J. Andrews, N. Baker, Xbox 360 system architecture. IEEE Micro 26(2), 25–37 (2006)

    Article  Google Scholar 

  12. C.S. Bamji, P. O’Connor, T. Elkhatib, S. Mehta, B. Thompson, L.A. Prather, D. Snow, O.C. Akkaya, A. Daniel, A.D. Payne, T. Perry, M. Fenton, V.-H. Chan, A 0.13 um cmos system-on-chip for a 512 × 424 time-of-flight image sensor with multi-frequency photo-demodulation up to 130 mhz and 2 gs/s adc. IEEE J. Solid-State Circuits 50(1), 303–319 (2015)

    Google Scholar 

  13. Y. Bar-Shalom, Tracking and Data Association (Academic Press Professional, Inc., San Diego, CA, 1987)

    MATH  Google Scholar 

  14. F. Bernardini, H.E. Rushmeier, The 3d model acquisition pipeline. Comput. Graphics Forum 21(2), 149–172 (2002)

    Article  Google Scholar 

  15. G. Borenstein, Making Things See: 3D Vision with Kinect, Processing, Arduino, and MakerBot (Maker Media, O’Reilly Media Inc., Sebastopol, 2012)

    Google Scholar 

  16. J.Y. Bouguet, Camera calibration toolbox for matlab. http://www.vision.caltech.edu/bouguetj/calib_doc/. Accessed March 2016

  17. J.Y. Bouguet, B. Curless, P. Debevec, M. Levoy, S. Nayar, S. Seitz, Overview of active vision techniques, in Proceedings of ACM SIGGRAPH Workshop, Course on 3D Photography (2000)

    Google Scholar 

  18. D. Claus, A.W. Fitzgibbon, A rational function lens distortion model for general cameras, in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2005)

    Google Scholar 

  19. B. Curless, M. Levoy, A volumetric method for building complex models from range images, in Proceedings of ACM SIGGRAPH (New York, 1996), pp. 303–312

    Google Scholar 

  20. B. Cyganek, An Introduction to 3D Computer Vision Techniques and Algorithms (Wiley, New York, 2007)

    MATH  Google Scholar 

  21. D. Dardari, A. Conti, U. Ferner, A. Giorgetti, M.Z. Win, Ranging with ultrawide bandwidth signals in multipath environments. Proc. IEEE 97(2), 404–426 (2009)

    Article  Google Scholar 

  22. E.R. Davies, Computer and Machine Vision, 4th edn. (Academic, Boston, 2012)

    Google Scholar 

  23. J. Davis, D. Nehab, R. Ramamoorthi, S. Rusinkiewicz, Spacetime stereo: a unifying framework for depth from triangulation, in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2003)

    Google Scholar 

  24. O. Faugeras, Three-Dimensional Computer Vision: A Geometric Viewpoint (MIT Press, Cambridge, 1993)

    Google Scholar 

  25. M.A. Fischler, R.C. Bolles, Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography, in Readings in Computer Vision: Issues, Problems, Principles and Paradigms, vol. 1 (M. Kaufmann Publishers, Los Altos, CA, 1987), pp. 726–740

    Google Scholar 

  26. D.A. Forsyth, J. Ponce, Computer Vision: A Modern Approach. Prentice Hall Professional Technical Reference (Prentice Hall, London, 2002)

    Google Scholar 

  27. A. Fusiello, Visione Computazionale. Tecniche di Ricostruzione Tridimensionale (Franco Angeli, Milano, 2013)

    Google Scholar 

  28. A. Fusiello, E. Trucco, A. Verri, A compact algorithm for rectification of stereo pairs. Mach. Vis. Appl. 12, 16–22 (2000)

    Article  Google Scholar 

  29. C. Harris, M. Stephens, A combined corner and edge detector. in Proceedings of Alvey Vision Conference (1988), pp. 147–151

    Google Scholar 

  30. R.I. Hartley, In defense of the eight-point algorithm. IEEE Trans. Pattern Anal. Mach. Intell. 19(6), 580–593 (1997)

    Article  Google Scholar 

  31. R.I. Hartley, P. Sturm, Triangulation, in Procedings of ARPA Image Understanding Workshop (1994)

    Google Scholar 

  32. R.I. Hartley, A. Zisserman, Multiple View Geometry in Computer Vision (Cambridge University Press, Cambridge, 2004)

    Book  MATH  Google Scholar 

  33. J. Heikkila, O. Silven, A four-step camera calibration procedure with implicit image correction, in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (1997)

    Google Scholar 

  34. A. Heyden, K. Astrom, Euclidean reconstruction from constant intrinsic parameters. in Proceedings of International Conference on Pattern Recognition, pp. 339–343

    Google Scholar 

  35. T.S. Huang, O. Faugeras, Some properties of the E matrix in two-view motion estimation. IEEE Trans. Pattern Anal. Mach. Intell. 11(12), 1310–1312 (1989)

    Article  Google Scholar 

  36. K. Konolige, Projected texture stereo, in Proceedings of IEEE International Conference on Robotics and Automation (2010)

    Google Scholar 

  37. K. Konolige, Sparse sparse bundle adjustment, in Proceedings of British Machine Vision Conference (BMVA Press, Aberystwyth, 2010), pp. 102.1–102.11

    Google Scholar 

  38. R. Lange, 3D Time-of-flight distance measurement with custom solid-state image sensors in CMOS/CCD-technology, Ph.D. thesis, University of Siegen (2000)

    Google Scholar 

  39. M. Levoy, K. Pulli, B. Curless, S. Rusinkiewicz, D. Koller, L. Pereira, M. Ginzton, S. Anderson, J. Davis, J. Ginsberg, J. Shade, D. Fulk, The digital michelangelo project: 3d scanning of large statues, in Proceedings of ACM SIGGRAPH (Addison-Wesley Publishing Co., New York, 2000), pp. 131–144

    Google Scholar 

  40. H. Li, R. Hartley, Five-point motion estimation made easy, in Proceedings of International Conference on Pattern Recognition (2006), pp. 630–633

    Google Scholar 

  41. C. Loop, Z. Zhang, Computing rectifying homographies for stereo vision, in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, (1999), p. 131

    Google Scholar 

  42. D.G. Lowe, Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)

    Article  Google Scholar 

  43. B.D. Lucas, T. Kanade, An iterative image registration technique with an application to stereo vision, in Proceedings of International Joint Conference on Artificial Intelligence, (Morgan Kaufmann Publishers Inc., San Francisco, CA, 1981), pp. 674–679

    Google Scholar 

  44. Q.T. Luong, O.D. Faugeras, The fundamental matrix: theory, algorithms, and stability analysis. Int. J. Comput. Vis. 17, 43–75 (1995)

    Article  Google Scholar 

  45. Y. Ma, S. Soatto, J. Kosecka, S.S. Sastry, An Invitation to 3-D Vision: From Images to Geometric Models (Springer, Berlin, 2003)

    MATH  Google Scholar 

  46. P.R.S. Mendonca, R. Cipolla, A simple technique for self-calibration in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (1999), p. 505

    Google Scholar 

  47. K. Mikolajczyk, C. Schmid, A performance evaluation of local descriptors. IEEE Trans. Pattern Anal. Mach. Intell. 27(10), 1615–1630 (2005)

    Article  Google Scholar 

  48. S.J.D. Prince, Computer Vision: Models, Learning, and Inference, 1st edn. (Cambridge University Press, New York, 2012)

    Book  MATH  Google Scholar 

  49. L. Robert, O. Faugeras, Relative 3d positioning and 3d convex hull computation from a weakly calibrated stereo pair, in Proceedings of International Conference on Computer Vision (1993), pp. 540–544

    Google Scholar 

  50. J. Salvi, J. Pagès, J. Batlle, Pattern codification strategies in structured light systems. Pattern Recogn. 37, 827–849 (2004)

    Article  MATH  Google Scholar 

  51. D. Scharstein, R. Szeliski, A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1–3), 7–42 (2001)

    MATH  Google Scholar 

  52. R. Schwarte et al., Pseudo-noise (pn) laser radar without scanner for extremely fast 3d-imaging and navigation, in Proceedings of Microwave and Optronics Conference (1997)

    Google Scholar 

  53. N. Snavely, Bundler, http://www.cs.cornell.edu/~snavely/bundler/. Accessed March 2016

  54. N. Snavely, S.M. Seitz, R. Szeliski, Modeling the world from internet photo collections. Int. J. Comput. Vis. 80(2), 189–210 (2008)

    Article  Google Scholar 

  55. G. Stockman, L.G. Shapiro, Computer Vision, 1st edn. (Prentice Hall PTR, Upper Saddle River, 2001)

    Google Scholar 

  56. D. Stoppa, F. Remondino (eds.), TOF Range-Imaging Cameras (Springer, Berlin, 2012)

    Google Scholar 

  57. R. Szeliski, Computer Vision: Algorithms and Applications (Springer, New York, 2010)

    MATH  Google Scholar 

  58. C. Tomasi, T. Kanade, Detection and tracking of point features. Technical report, International Journal of Computer Vision (1991)

    Google Scholar 

  59. B. Triggs, P.F. McLauchlan, R.I. Hartley, A.W. Fitzgibbon, Bundle adjustment - a modern synthesis, in Proceedings of ICCV Workshop, Vision Algorithms: Theory and Practice (Springer, London, 2000), pp. 298–372

    Google Scholar 

  60. M. Trobina, Error model of a coded-light range sensor. Technical report, Communication Technology Laboratory Image Science Group, ETH-Zentrum (1995)

    Google Scholar 

  61. E. Trucco, A. Verri, Introductory Techniques for 3-D Computer Vision (Prentice Hall PTR, Upper Saddle River, 1998)

    Google Scholar 

  62. Z. Xu, Investigation of 3D-Imaging Systems Based on Modulated Light and Optical RF-Interferometry (Shaker Verlag GmbH, Aachen, 1999)

    Google Scholar 

  63. Z. Zhang, T. Kanade, Determining the epipolar geometry and its uncertainty: a review. Int. J. Comput. Vis. 27, 161–195 (1998)

    Article  Google Scholar 

  64. L. Zhang, B. Curless, S.M. Seitz, Rapid shape acquisition using color structured light and multi-pass dynamic programming, in Proceedings of IEEE International Symposium on 3D Data Processing, Visualization, and Transmission (2002), pp. 24–36

    Google Scholar 

  65. L. Zhang, B. Curless, S.M. Seitz, Spacetime stereo: shape recovery for dynamic scenes, in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2003)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Zanuttigh, P., Marin, G., Dal Mutto, C., Dominio, F., Minto, L., Cortelazzo, G.M. (2016). Introduction. In: Time-of-Flight and Structured Light Depth Cameras. Springer, Cham. https://doi.org/10.1007/978-3-319-30973-6_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-30973-6_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-30971-2

  • Online ISBN: 978-3-319-30973-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics