Skip to main content
SpringerLink
Log in

Binocular Stereo

  • Living reference work entry
  • First Online:
Computer Vision

  • 164 Accesses

Synonyms

Binocular stereo vision; Stereo matching

Related Concepts

Definition

Binocular stereo refers to the task of recovering depths of a static scene using a pair of overlapping images captured from different viewpoints. Binocular stereo systems usually use two identical parallel cameras that are horizontally separated by a certain distance, referred to as the baseline. The task of binocular stereo amounts to finding dense pixel correspondences between the image pair along horizontal scan lines (called epipolar lines) or estimating the disparity for each pixel of the stereo images. The outcome of binocular stereo takes a form of a depth map that can be computed from disparity given the baseline and focal length of a stereo system or instead of a disparity map itself.

Background

Binocular stereo is one of the oldest topics in computer vision. Similar...

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

Access this chapter

Log in via an institution

Institutional subscriptions

Notes

  1. 1.

    Note that in segment-based methods, the objective function in Eq. 6 is modified so that each node p and variable D p represents a superpixel and its disparity plane assignment, respectively.

  2. 2.

    http://vision.middlebury.edu/stereo/

  3. 3.

    http://www.cvlibs.net /datasets/kitti/eval_scene_flow.php?benchmark= stereo

  4. 4.

    http://www.cvlibs.net /datasets/kitti/eval_stereo_flow.php?benchmark= stereo

  5. 5.

    https://www.eth3d.net

  6. 6.

    https://lmb.informatik.uni- freiburg.de/resources/datasets/SceneFlowDatasets. en.html

References

  1. Besse F, Rother C, Fitzgibbon AW, Kautz J (2014) PMBP: patchMatch belief propagation for correspondence field estimation. Intl J Comput Vis (IJCV) 110(1):2–13

    Article  Google Scholar 

  2. Birchfield S, Tomasi C (1999) Multiway cut for stereo and motion with slanted surfaces. In: Proceedings of international conference on computer vision (ICCV), pp 489–495

    Google Scholar 

  3. Bleyer M, Rhemann C, Rother C (2011). PatchMatch stereo – stereo matching with slanted support windows. In: Proceedings of British machine vision conference (BMVC), pp 1–11

    Google Scholar 

  4. Drory A, Haubold C, Avidan S, Hamprecht FA (2014) Semi-global matching: a principled derivation in terms of message passing. In: Proceedings of German Conference on Pattern Recognition (GCPR), pp 43–53

    Google Scholar 

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

    Article  Google Scholar 

  6. Kendall A, Martirosyan H, Dasgupta S, Henry P, Kennedy R, Bachrach A, Bry A (2017) End-to-end learning of geometry and context for deep stereo regression. In: Proceedings of international conference on computer vision (ICCV)

    Google Scholar 

  7. Kolmogorov V, Zabih R (2001) Computing visual correspondence with occlusions using graph cuts. In: Proceedings of international conference on computer vision (ICCV), vol 2, pp 508–515

    Google Scholar 

  8. Lucas BD, Kanade T (1981) An iterative image registration technique with an ap-plication to stereo vision. In: Hayes PJ (ed) Proceedings of international joint conference on artificial intelligence (IJCAI), pp 674–679

    Google Scholar 

  9. Mayer N, Ilg E, Hausser P, Fischer P, Cremers D, Dosovitskiy A, Brox T (2016) A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation. In: Proceedings of IEEE conference on computer vision and pattern recognition (CVPR)

    Google Scholar 

  10. Olsson C, Ulen J, Boykov Y (2013) In defense of 3D-label stereo. In: Proceedings of IEEE conference on computer vision and pattern recognition (CVPR), pp 1730–1737

    Google Scholar 

  11. Scharstein D, Szeliski R (2002) A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int J Comput Vis (IJCV) 47(1/2/3): 7–42

    Google Scholar 

  12. Scharstein D, Taniai T, Sinha SN (2017) Semi-global stereo matching with surface orientation priors. In: Proceedings of 2017 international conference on 3D vision (3DV), Qingdao, pp 215–224

    Google Scholar 

  13. Szeliski R, Zabih R, Scharstein D, Veksler O, Kolmogorov V, Agarwala A, Tappen M, Rother C (2008) A comparative study of energy minimization methods for Markov random fields with smoothness-based priors. IEEE Trans Pattern Anal Mach Intell (PAMI) 30(6):1068–1080

    Article  Google Scholar 

  14. Taniai T, Matsushita Y, Sato Y, Naemura T (2018) Continuous 3D label stereo matching using local expansion moves. IEEE Trans Pattern Anal Mach Intell (TPAMI) 40(11):2725–2739

    Article  Google Scholar 

  15. Wei Y, Quan L (2005) Asymmetrical occlusion handling using graph cut for multi-view stereo. In: Proceedings of IEEE conference on computer vision and pattern recognition (CVPR), pp 902–909

    Google Scholar 

  16. Woodford O, Torr P, Reid I, Fitzgibbon A (2009) Global stereo reconstruction under second-order smoothness priors. IEEE Trans. Pattern Anal Mach Intell (PAMI) 31(12):2115–2128

    Article  Google Scholar 

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

    Article  Google Scholar 

  18. Zabih R, Woodfill J (1994) Non-parametric local transforms for computing visual correspondence. In: Proceedings of European conference on computer vision (ECCV), pp 151–158

    Google Scholar 

  19. Zbontar J, LeCun Y (2016) Stereo matching by training a convolutional neural network to compare image patches. J Mach Learn Res 17:1–32

    MATH  Google Scholar 

  20. Zhang F, Prisacariu V, Yang R, Torr PH (2019) GA-Net: guided aggregation net for end-to-end stereo matching. In: Proceedings of IEEE conference on computer vision and pattern recognition (CVPR)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Research Administrative Division, OMRON SINIC X Corporation, Tokyo, Japan

    Tatsunori Taniai

  2. Discrete Optimization Unit, RIKEN Center for Advanced Intelligence Project, Tokyo, Japan

    Tatsunori Taniai

Authors
  1. Tatsunori Taniai
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tatsunori Taniai .

Section Editor information

  1. Microsoft, Redmond, WA, USA

    Sudipta N. Sinha

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

Taniai, T. (2020). Binocular Stereo. In: Computer Vision. Springer, Cham. https://doi.org/10.1007/978-3-030-03243-2_809-1

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-03243-2_809-1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-03243-2

  • Online ISBN: 978-3-030-03243-2

  • eBook Packages: Springer Reference Computer SciencesReference Module Computer Science and Engineering

Publish with us

Policies and ethics

Search

Navigation