Skip to main content
SpringerLink
Log in

SymStereo: Stereo Matching using Induced Symmetry

  • Published:
International Journal of Computer Vision Aims and scope Submit manuscript

Abstract

Stereo methods always require a matching function for assessing the likelihood of two pixels being in correspondence. Such functions, commonly referred as matching costs, measure the photo-similarity (or dissimilarity) between image regions centered in putative matches. This article proposes a new family of stereo cost functions that measure symmetry instead of photo-similarity for associating pixels across views. We start by observing that, given two stereo views and an arbitrary virtual plane passing in-between the cameras, it is possible to render image signals that are either symmetric or anti-symmetric with respect to the contour where the virtual plane meets the scene. The fact is investigated in detail and used as cornerstone to develop a new stereo framework that relies in symmetry cues for solving the data association problem. Extensive experiments in dense stereo show that our symmetry-based cost functions compare favorably against the best performing photo-similarity matching costs. In addition, we investigate the possibility of accomplishing Stereo Rangefinding that consists in using passive stereo to exclusively recover depth along a pre-defined scan plane. Thorough experiments provide evidence that stereo from induced symmetry is specially well suited for this purpose.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22

Similar content being viewed by others

Notes

  1. In Sun et al. (2005), the term symmetry is employed with a completely different meaning, referring to the equal treatment of left and right views.

  2. We obtain slightly worse results with SGM but, on the other hand, the results accomplished with GC are slightly better.

References

  • Ansar, A., Castano, A., Matthies, L. (2004). Enhanced real-time stereo using bilateral filtering. In 3D data processing, visualization and transmission.

  • Antunes, M., & Barreto, J. P. (2011). Stereo estimation of depth along virtual cut planes. In International conference on computer vision workshop (CVVT).

  • Antunes, M., & Barreto, J. P. (2012). Semi-dense piecewise planar stereo reconstruction using symstereo and pearl. In 3DimPVT–3D data processing, visualization and transmission (pp. 1–8), October.

  • Antunes, M., Barreto, J. P., Premebida, C., & Nunes, U. (2012). Can stereo vision replace a laser rangefinder? In IEEE international conference in intelligent robot systems (pp. 1–8).

  • Antunes, M., Barreto, J. P., & Zabulis, X. (2011). Plane surface detection and reconstruction using induced stereo symmetry. In British machine vision conference.

  • Banks, J., & Corke, P. (2001). Quantitative evaluation of matching methods and validity measures for stereo vision. The International Journal of Robotics Research, 20(7), 512–532.

    Article  Google Scholar 

  • Birchfield, S., & Tomasi, C. (1998). A pixel dissimilarity measure that is insensitive to image sampling. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(4), 401–406.

    Article  Google Scholar 

  • Boykov, Y., & Kolmogorov, V. (2004). An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(9), 1124–1137.

    Article  Google Scholar 

  • Boykov, Y., Veksler, O., & Zabih, R. (2001). Fast approximate energy minimization via graph cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(11), 1222–1239.

    Article  Google Scholar 

  • Brown, M. Z., Burschka, D., & Hager, G. D. (2003). Advances in computational stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(8), 993–1008.

  • Collins, R. T. (1996). A space-sweep approach to true multi-image matching. In IEEE conference on computer vision and pattern recognition.

  • Fookes, C., Maeder, A., Sridharan, S., & Cook, J. (2004). Multi-spectral stereo image matching using mutual information. In 3D data processing, visualization and transmission.

  • Gallup, D., Frahm, J.-M., Mordohai, P., Yang, Q., & Pollefeys, M. (2007). Real-time plane-sweeping stereo with multiple sweeping directions. In IEEE conference on computer vision and pattern recognition.

  • Gautama, S., Lacroix, S., & Devy, M. (1999). Evaluation of stereo matching algorithms for occupant detection. In Proceedings of the international workshop on recognition, analysis, and tracking of faces and gestures in real-time systems.

  • Gong, M., Yang, R., Wang, L., & Gong, M. (2007). A performance study on different cost aggregation approaches used in real-time stereo matching. International Journal of Computer Vision, 75(2), 283–296.

  • Hirschmüller, H. (2005). Accurate and efficient stereo processing by semi-global matching and mutual information. In IEEE conference on computer vision and pattern recognition.

  • Hirschmüller, H., & Scharstein, D (2009). Evaluation of stereo matching costs on images with radiometric differences. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(9), 1582–1599.

  • Kolmogorov, V., & Zabih, R. (2002). What energy functions can be minimized via graph cuts? In European conference on computer vision.

  • Kovesi, P. (1995). Image features from phase congruency. Technical report, Videre.

  • Kovesi, P. (1997). Symmetry and asymmetry from local phase. In Tenth Australian joint conference on artificial intelligence.

  • Liu, Y., Hel-Or, H., Kaplan, C. S., Van Gool, L. J. (2010). Computational symmetry in computer vision and computer graphics. In Foundations and Trends in Computer Graphics and Vision.

  • Ma, Y., Soatto, S., Kosecka, J., & Sastry, S. S. (2003). An invitation to 3-D vision: From images to geometric models. Berlin: Springer Verlag.

  • Mordohai, P. (2009). The self-aware matching measure for stereo. In International conference on vomputer vision.

  • Ponce, J., McHenry, K., Papadopoulo, T., Teillaud, M., & Triggs, B. (2005). On the absolute quadratic complex and its application to autocalibration. In IEEE conference on computer vision and pattern recognition.

  • Sarkar, I., & Bansal, M. (2007). A wavelet-based multiresolution approach to solve the stereo correspondence problem using mutual information. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 37(4), 1009–1014.

    Article  Google Scholar 

  • Scharstein, D., & Pal, C. (2007). Learning conditional random fields for stereo. In IEEE conference on computer vision and pattern recognition.

  • 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.

  • Strecha, C., von Hansen, W., Van Gool, L. J., Fua, P., & Thoennessen, U. (2008). On benchmarking camera calibration and multi-view stereo for high resolution imagery. In IEEE conference on computer vision and pattern recognition.

  • Sun, J., Li, Y., Kang, S. B., & Shum, H.-Y. (2005). Symmetric stereo matching for occlusion handling. In IEEE conference on computer vision and pattern recognition.

  • Szeliski, R., & Scharstein, D. (2004). Sampling the disparity space image. IEEE Transactions Pattern Analysis and Machine Intelligence, 26(3), 419 425.

  • Szeliski, R., Zabih, R., Scharstein, D., Veksler, O., Kolmogorov, V., Agarwala, A., et al. (2008). A comparative study of energy minimization methods for markov random fields with smoothness-based priors. EEE Transactions on Pattern Analysis and Machine Intelligence, 30(6), 1068–1080.

    Article  Google Scholar 

  • Tombari, F., Mattoccia, S., Di Stefano, L., & Addimanda, E. (2008). Classification and evaluation of cost aggregation methods for stereo correspondence. In IEEE conference on computer vision and pattern recognition.

  • Wang, L., Gong, M., Gong, M., Yang, R. (2006). How far can we go with local optimization in real-time stereo matching. In 3D data processing, visualization and transmission.

  • Yoon, K. J., Student Member, & Kweon, I. S. (2006). Adaptive support-weight approach for correspondencesearch. IEEE Transactions Pattern Analysis and MachineIntelligence, 2006.

  • Zabih, R., Woodfill, J. (1994). Non-parametric local transforms for computing visual correspondence. In European conference of computer vision.

Download references

Acknowledgments

Michel Antunes acknowledges the Portuguese Science Foundation (FCT) that generously funded his PhD work through grant SFRH/BD/47488/2008. Joao P Barreto thanks Google, Inc for the support through a “Faculty Research Award” and FCT for generous funding through grants PDCS10:PTDC/EEA-AUT/113818/2009 and AMS-HMI12: RECI/EEI-AUT/0181/2012.

Author information

Authors and Affiliations

  1. Institute of Systems and Robotics, Department of Electrical and Computer Engineering, University of Coimbra, Coimbra, 3030, Portugal

    Michel Antunes & João P. Barreto

Authors
  1. Michel Antunes
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. João P. Barreto
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Michel Antunes.

Additional information

Communicated by K. Ikeuchi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Antunes, M., Barreto, J.P. SymStereo: Stereo Matching using Induced Symmetry. Int J Comput Vis 109, 187–208 (2014). https://doi.org/10.1007/s11263-014-0715-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11263-014-0715-7

Keywords

Search

Navigation