Skip to main content
SpringerLink
Log in

A Fast TGV-l 1 RGB-D Flow Estimation

  • Conference paper
Advances in Visual Computing (ISVC 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8887))

Included in the following conference series:

  • 3672 Accesses

Abstract

We present a novel method for fast and dense 3D scene flow estimation which optimizes consistency and smoothness in both intensity and depth data while considering computing efficiency for the real-world applications. 3D scene flow estimation is an attractive problem with the advent of commodity RGB-D cameras. Naive extensions of recent variational optical flow techniques show promising but limited successes. Due to their primitive priors, solutions from total variation approaches prefer unrealistic constant motion. To overcome these problems and consider the computational efficiency, we adopt an image-guided total generalized variation (ITGV) regularization. As demonstrated with experimental results, the proposed method outperforms both in terms of accuracy and speed compared to the existing variational approaches.

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 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight 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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Vedula, S., Rander, P., Collins, R., Kanade, T.: Three-dimensional scene flow. IEEE Transactions on Pattern Analysis and Machine Intelligence 27, 475–480 (2005)

    Article  Google Scholar 

  2. Huguet, F., Devernay, F.: A variational method for scene flow estimation from stereo sequences. In: IEEE International Conference on Computer Vision, pp. 1–7 (2007)

    Google Scholar 

  3. Lukins, T.C., Fisher, R.B.: Colour constrained 4d flow. In: British Machine Vision Conference (2005)

    Google Scholar 

  4. Spies, H., Jähne, B., Barron, J.L.: Range flow estimation. Computer Vision and Image Understanding 85, 209–231 (2002)

    Article  MATH  Google Scholar 

  5. Quiroga, J., Devernay, F., Crowley, J.L., et al.: Local/global scene flow estimation. In: IEEE International Conference on Image Processing (2013)

    Google Scholar 

  6. Herbst, E., Ren, X., Fox, D.: Rgb-d flow: Dense 3-d motion estimation using color and depth. In: IEEE International Conference on Robotics and Automation, pp. 2276–2282 (2013)

    Google Scholar 

  7. Gottfried, J.M., Fehr, J., Garbe, C.S.: Computing range flow from multi-modal kinect data. In: Bebis, G., et al. (eds.) ISVC 2011, Part I. LNCS, vol. 6938, pp. 758–767. Springer, Heidelberg (2011)

    Google Scholar 

  8. Hadfield, S., Bowden, R.: Kinecting the dots: Particle based scene flow from depth sensors. In: IEEE International Conference on Computer Vision, pp. 2290–2295 (2011)

    Google Scholar 

  9. Hadfield, S., Bowden, R.: Scene particles: Unregularized particle based scene flow estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence (2013)

    Google Scholar 

  10. Bruhn, A., Weickert, J., Schnörr, C.: Lucas/kanade meets horn/schunck: Combining local and global optic flow methods. International Journal of Computer Vision 61, 211–231 (2005)

    Article  Google Scholar 

  11. Rudin, L.I., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Physica D: Nonlinear Phenomena 60, 259–268 (1992)

    Article  MATH  Google Scholar 

  12. Bredies, K., Kunisch, K., Pock, T.: Total generalized variation. SIAM Journal on Imaging Sciences 3, 492–526 (2010)

    Article  MATH  MathSciNet  Google Scholar 

  13. Werlberger, M., Trobin, W., Pock, T., Wedel, A., Cremers, D., Bischof, H.: Anisotropic huber-l1 optical flow. In: British Machine Vision Conference, pp. 1–11 (2009)

    Google Scholar 

  14. Chambolle, A., Pock, T.: A first-order primal-dual algorithm for convex problems with applications to imaging. Journal of Mathematical Imaging and Vision 40, 120–145 (2011)

    Article  MATH  MathSciNet  Google Scholar 

  15. Werlberger, M.: Convex Approaches for High Performance Video Processing. PhD thesis, Institute for Computer Graphics and Vision, Graz University of Technology (2012)

    Google Scholar 

  16. Ranftl, R., Gehrig, S., Pock, T., Bischof, H.: Pushing the limits of stereo using variational stereo estimation. In: 2012 IEEE Intelligent Vehicles Symposium (IV), pp. 401–407. IEEE (2012)

    Google Scholar 

  17. Basha, T., Moses, Y., Kiryati, N.: Multi-view scene flow estimation: A view centered variational approach. International Journal of computer Vision 101, 6–21 (2013)

    Article  MATH  MathSciNet  Google Scholar 

  18. Baker, S., Scharstein, D., Lewis, J., Roth, S., Black, M.J., Szeliski, R.: A database and evaluation methodology for optical flow. International Journal of Computer Vision 92, 1–31 (2011)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Imaging Media Research Center, Korea Institute of Science and Technology, South Korea

    Junha Roh, Hwasup Lim & Sang Chul Ahn

Authors
  1. Junha Roh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hwasup Lim
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sang Chul Ahn
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Nevada at Reno, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, CA, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, NV, USA

    Darko Koracin

  5. The University of Texas at Dallas, 75080, Richardson, TX, USA

    Ryan McMahan

  6. NextGen Interactions, 27604, Raleigh, NC, USA

    Jason Jerald

  7. Indiana University, 46202, Indianapolis, IN, USA

    Hui Zhang

  8. Microsoft Research, 1 Microsoft Way, 98052, Redmond, WA, USA

    Steven M. Drucker

  9. University of Delaware, 19716-2712, Newark, DE, USA

    Chandra Kambhamettu

  10. Intel Corp., 95054, Santa Clara, CA, USA

    Maha El Choubassi

  11. Computer Graphics and Interactive Media Lab, Department of Computer Science, University of Houston, 77004, Houston, TX, USA

    Zhigang Deng

  12. NVIDIA, 34788, Leesburg, FL, USA

    Mark Carlson

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Roh, J., Lim, H., Ahn, S.C. (2014). A Fast TGV-l 1 RGB-D Flow Estimation. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2014. Lecture Notes in Computer Science, vol 8887. Springer, Cham. https://doi.org/10.1007/978-3-319-14249-4_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-14249-4_15

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-14248-7

  • Online ISBN: 978-3-319-14249-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation