Skip to main content
SpringerLink
Log in

TOF Cameras and Stereo Systems: Comparison and Data Fusion

  • Chapter
  • First Online:
TOF Range-Imaging Cameras

Abstract

Time-Of-Flight range cameras and stereo vision systems (for simplicity called TOF cameras and stereo systems now on) are both depth acquisition devices capable to collect 3M information of dynamic scenes. In spite they can be used for similar tasks in many applications, it would not be appropriate to view the two systems as alternate or even competitive choices, since their characteristics and actual capability are markedly different. Indeed synergically combining together TOF cameras and stereo systems is a rather intriguing and useful option. This chapter firstly compares Time-Of-Flight range cameras and stereo vision systems, and then addresses the problem of fusing the data produced by the two systems. Because of the many aspects involved, the comparison is all but straightforward and could be certainly organized in different ways. The proposed one represents a systematic approach.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 179.99
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

References

  1. T. Luhmann, S. Robson, S. Kyle, I. Harley, Close Range Photogrammetry: Principles, Techniques and Applications, (Wiley, Chichester, 2007), pp. 528 ISBN 978-0-47010-633-4

    Google Scholar 

  2. E.M. Mikhail, J.S. Bethel, J.C. McGlone, Introduction to Modern Photogrammetry, (Wiley, New York, 2001), pp. 496 ISBN 978-0-47130-924-6

    Google Scholar 

  3. Point Grey Research, Inc., http://www.ptgrey.com/products/stereo.asp

  4. TYZX, Inc., http://www.tyzx.com

  5. Middlebury Stereo Vision Page, http://vision.middlebury.edu/stereo/

  6. R. Szeliski, Computer Vision, Algorithms and Applications, Series: Texts in Computer Science, 1st edn. (Springer, New York, 2011) pp. 812 ISBN 978-1-84882-934-3

    Google Scholar 

  7. G. Bradski, A. Kaehler, Learning OpenCV: Computer Vision with the OpenCV Library, 1st edn. (O’Reilly Media, Sebastopol, 2008) pp. 555 ISBN 978-0596516130

    Google Scholar 

  8. J.-Y. Bouguet, Camera Calibration Toolbox for Matlab, http://www.vision.caltech.edu/bouguetj/calib_doc/

  9. OpenCV, Open Source Computer Vision library, http://opencv.willowgarage.com/wiki/

  10. A. Fusiello, E. Trucco, A. Verri, A compact algorithm for rectification of stereo pairs, machine vision and applications 12(1) (Springer Berlin/Heidelberg, 2000) pp. 16–22 ISSN: 0932-8092

    Google Scholar 

  11. D. Scharstein, R. Szeliski, A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vis. 47(1–3) (Kluwer Academic Publishers, Hingham, 2002), pp. 7–42 ISSN 0920-5691

    Google Scholar 

  12. A.W. Gruen, Adaptive least squares correlation: a powerful image matching technique. South African J Photogramm, Remote Sens. Cartogr. 14, 175–187 (1985)

    Google Scholar 

  13. F.C. Crow, Summed-area tables for texture mapping, Proceedings of the 11th annual conference on Computer graphics and interactive techniques, SIGGRAPH 84, (ACM, New York, 1984), pp. 207–212 ISBN 0-89791-138-5

    Google Scholar 

  14. M.J. McDonnell, Box-filtering techniques, computer graphics and image processing, 17(1), 65–70 (1981). ISSN 0146-664X, 10.1016/S0146-664X(81)80009-3

  15. T. Kanade, M. Okutomi, A stereo matching algorithm with an adaptive window: theory and experiment, IEEE transactions on pattern analysis and machine intelligence, 16, N. 1 920–932, (1994)

    Google Scholar 

  16. A. Fusiello, V. Roberto, E. Trucco, Symmetric stereo with multiple windowing. Int. J. Pattern Recognit. Artif. Intell. 14, 1053–1066 (2000)

    Google Scholar 

  17. K.-J. Yoon, I.S. Kweon, Adaptive support-weight approach for correspondence search. IEEE Trans. Pattern Anal. Mach. Intell. 28(4), 650–656 (2006)

    Article  Google Scholar 

  18. F. Tombari, S. Mattoccia, L. Di Stefano, Segmentation-based adaptive support for accurate stereo correspondence, in Proceedings of IEEE Pacific-Rim Symposium on Image and Video Technology, vol 1 December 17–19 (Santiago, Chile, 2007), pp. 427–438, Springer

    Google Scholar 

  19. J. Sun, N.-N. Zheng, H.-Y. Shum, Stereo matching using belief propagation. IEEE Trans. Pattern Anal. Mach. Intell. 25(7), 787–800 (2003)

    Article  Google Scholar 

  20. R. Szeliski, R. Zabih, D. Scharstein, O. Veksler, A. Agarwala, C. Rother, A comparative study of energy minimization methods for Markov random fields, ECCV, 2006, pp. 16–29

    Google Scholar 

  21. S.Z. Li, Markov random field modelling in image analysis (Advances in Pattern Recognition), 3rd edn. (Springer, London, 2009). ISBN 1848002785

    Google Scholar 

  22. I.J. Cox, S.L. Hingorani, S.B. Rao, B.M. Maggs, A maximum likelihood stereo algorithm. Comput. Vis. Image Underst. 63, 542–567 (1996)

    Article  Google Scholar 

  23. H. Hirschmuller, Stereo processing by semiglobal matching and mutual information. IEEE Trans. Pattern Anal. Mach. Intell. 30(2), 328–341 (2008)

    Article  Google Scholar 

  24. M. Burns, G.W. Roberts, Mixed-Signal IC test and measurement (The Oxford series in electrical and computer engineering) (Oxford University Press, New York, 2001)

    Google Scholar 

  25. Evaluation of measurement data—Guide to the expression of uncertainty in measurement, Joint Committee for Guides in Metrology, JCGM 100:2008

    Google Scholar 

  26. T. Kahlmann, H. Ingensand, Calibration and development for increased accuracy of 3D range imaging cameras, J. Appl. Geodesy, 2(1), 1–11 (2008), ISSN (Online) 1862-9024, ISSN (Print) 1862-9016

    Google Scholar 

  27. MESA Imaging, http://www.mesa-imaging.ch/

  28. J. Davis, D. Nehab, R. Ramamoorthi, S. Rusinkiewicz, Spacetime stereo: A unifying framework for depth from triangulation, CVPR, 359–366 (2003)

    Google Scholar 

  29. L. Zhang, B. Curless, S. M. Seitz, Spacetime stereo: Shape recovery for dynamic scenes. In IEEE Computer Society, CVPR, 2003, pp. 367–374

    Google Scholar 

  30. C. Dal Mutto, P. Zanuttigh, G.M. Cortelazzo, A Probabilistic Approach to TOF and stereo data fusion, 3DPVT (France, Paris, 2010)

    Google Scholar 

  31. J. Zhu, L. Wang, R. Yang, J. Davis, Fusion of Time-Of-Flight depth and stereo for high accuracy depth maps. CVPR, 23–28 June 2008

    Google Scholar 

  32. B.K.P. Horn, Closed-form solution of absolute orientation using unit quaternions. J. Opt. Soc. America 4(4), 629–642 (1987)

    Article  Google Scholar 

  33. M.A. Fischler, R.C. Bolles, Random Sample Consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Comm. ACM 24, 381–395 (1981)

    Article  Google Scholar 

  34. K.-D. Kuhnert, M. Stommel, Fusion of stereo-camera and PMD-camera data for real-time suited precise 3D environment reconstruction, IEEE/RSJ international conference on intelligent robots and systems, 9–15 Oct 2006, pp. 4780–4785

    Google Scholar 

  35. Q. Yang, K.-H. Tan, B. Culbertson, J. Apostolopoulos, Fusion of active and passive sensors for fast 3D capture, IEEE international workshop on multimedia signal processing (MMSP), pp. 69–74, 4–6 Oct 2010

    Google Scholar 

  36. J. Zhu, L. Wang, R. Yang, J. E Davis, Z. Pan, Reliability fusion of Time-Of-Flight depth and stereo geometry for high quality depth maps, IEEE Trans. Pattern Anal. Mach. Intell. 33(7), ISSN 0162-8828, (IEEE Computer Society, Washington, 2011) pp. 1400–1414

    Google Scholar 

  37. J. Zhu, L. Wang, J. Gao, R. Yang, Spatial-temporal fusion for high accuracy depth maps using dynamic MRFs. IEEE Trans. Pattern Anal. Mach. Intell. 32(5), 899–909 (2010)

    Article  Google Scholar 

  38. S. A. Gudmundsson, H. Aanaes, R. Larsen, Fusion of Stereo Vision and Time-Of-Flight Imaging for Improved 3D Estimation. Int. J. Intell. Syst. Technol. Appl. 5(3/4), 425–433, (2008) ISSN 1740–8865, (Inderscience Publishers, Geneva, Switzerland 2008)

    Google Scholar 

  39. J. Fischer, G. Arbeiter, A. Verl, Combination of Time-Of-Flight depth and stereo using semiglobal optimization, IEEE international conference on robotics and automation (ICRA), pp. 3548–3553, 9–13 May 2011

    Google Scholar 

  40. S. Birchfield, C. Tomasi, A pixel dissimilarity measure that is insensitive to image sampling, IEEE Trans. Pattern Anal. Mach. Intell. 20(4), 401–406, N. 6 (1998), ISSN 0162-8828

    Google Scholar 

  41. C.M. Bishop, Pattern recognition and machine learning (Information Science and Statistics) (Springer, New York, 2006). ISBN 0387310738, 9780387310732

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. University of Padua, Padua, Italy

    Carlo Dal Mutto, Pietro Zanuttigh & Guido M. Cortelazzo

Authors
  1. Carlo Dal Mutto
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pietro Zanuttigh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Guido M. Cortelazzo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Carlo Dal Mutto .

Editor information

Editors and Affiliations

  1. , 3D Optical Metrology (3DOM), Fondazione Bruno Kessler, Trento, Italy

    Fabio Remondino

  2. , Smart Optical Sensors and Interfaces, Fondazione Bruno Kessler, Trento, Italy

    David Stoppa

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Mutto, C.D., Zanuttigh, P., Cortelazzo, G.M. (2013). TOF Cameras and Stereo Systems: Comparison and Data Fusion. In: Remondino, F., Stoppa, D. (eds) TOF Range-Imaging Cameras. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27523-4_9

Download citation

Publish with us

Policies and ethics

Search

Navigation