Advertisement

High-Resolution Stereo Datasets with Subpixel-Accurate Ground Truth

  • Daniel ScharsteinEmail author
  • Heiko Hirschmüller
  • York Kitajima
  • Greg Krathwohl
  • Nera Nešić
  • Xi Wang
  • Porter Westling
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8753)

Abstract

We present a structured lighting system for creating high-resolution stereo datasets of static indoor scenes with highly accurate ground-truth disparities. The system includes novel techniques for efficient 2D subpixel correspondence search and self-calibration of cameras and projectors with modeling of lens distortion. Combining disparity estimates from multiple projector positions we are able to achieve a disparity accuracy of 0.2 pixels on most observed surfaces, including in half-occluded regions. We contribute 33 new 6-megapixel datasets obtained with our system and demonstrate that they present new challenges for the next generation of stereo algorithms.

Keywords

Code Image Gray Code Bundle Adjustment Lens Distortion Disparity Estimate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This work was supported by NSF awards IIS-0917109 and IIS-1320715 to DS.

References

  1. 1.
    3dMD: 3D imaging systems. http://www.3dmd.com/
  2. 2.
    Acute 3D: 3D photogrammetry software. http://www.acute3d.com/
  3. 3.
    Batlle, J., Mouaddib, E., Salvi, J.: Recent progress in coded structured light as a technique to solve the correspondence problem: a survey. Pat. Rec. 31(7), 963–982 (1998)CrossRefGoogle Scholar
  4. 4.
    Besl, P.: Active optical range imaging sensors. MV&A 1(2), 127–152 (1988)Google Scholar
  5. 5.
    Brown, M., Burschka, D., Hager, G.: Advances in computational stereo. TPAMI 25(8), 993–1008 (2003)CrossRefGoogle Scholar
  6. 6.
    Davis, J., Nehab, D., Ramamoorthi, R., Rusinkiewicz, S.: Spacetime stereo: a unifying framework for depth from triangulation. TPAMI 27(2), 296–302 (2005)CrossRefGoogle Scholar
  7. 7.
    Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? The KITTI vision benchmark suite. In: CVPR, pp. 3354–3361 (2012)Google Scholar
  8. 8.
    Geiger, A., Roser, M., Urtasun, R.: Efficient large-scale stereo matching. In: Kimmel, R., Klette, R., Sugimoto, A. (eds.) ACCV 2010, Part I. LNCS, vol. 6492, pp. 25–38. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  9. 9.
    Gupta, M., Agrawal, A., Veeraraghavan, A., Narasimhan, S.: A practical approach to 3D scanning in the presence of interreflections, subsurface scattering and defocus. IJCV 102(1–3), 33–55 (2013)CrossRefGoogle Scholar
  10. 10.
    Gupta, M., Nayar, S.: Micro phase shifting. In: CVPR, pp. 813–820 (2012)Google Scholar
  11. 11.
    Hansen, P., Alismail, H., Rander, P., Browning, B.: Online continuous stereo extrinsic parameter estimation. In: CVPR, pp. 1059–1066 (2012)Google Scholar
  12. 12.
    Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge University Press, Cambridge (2000)zbMATHGoogle Scholar
  13. 13.
    Hirschmüller, H., Gehrig, S.: Stereo matching in the presence of sub-pixel calibration errors. In: CVPR, pp. 437–444 (2009)Google Scholar
  14. 14.
    Hirschmüller, H., Scharstein, D.: Evaluation of stereo matching costs on images with radiometric differences. TPAMI 31(9), 1582–1599 (2009)CrossRefGoogle Scholar
  15. 15.
    Hirschmüller, H.: Stereo processing by semiglobal matching and mutual information. TPAMI 30(2), 328–341 (2008)CrossRefGoogle Scholar
  16. 16.
    Hirschmüller, H., Innocent, P., Garibaldi, J.: Real-time correlation-based stereo vision with reduced border errors. IJCV 47(1–3), 229–246 (2002)CrossRefzbMATHGoogle Scholar
  17. 17.
    Hosni, A., Bleyer, M., Gelautz, M.: Secrets of adaptive support weight techniques for local stereo matching. CVIU 117(6), 620–632 (2013)Google Scholar
  18. 18.
    Hu, X., Mordohai, P.: A quantitative evaluation of confidence measures for stereo vision. TPAMI 34(11), 2121–2133 (2012)CrossRefGoogle Scholar
  19. 19.
    Izadi, S., et al.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: ACM UIST, pp. 559–568 (2011)Google Scholar
  20. 20.
    Lazaros, N., Sirakoulis, G., Gasteratos, A.: Review of stereo vision algorithms: from software to hardware. J. Optomechatron. 2(4), 435–462 (2008)CrossRefGoogle Scholar
  21. 21.
    Levoy, M., et al.: The digital Michelangelo project: 3D scanning of large statues. In: SIGGRAPH, pp. 131–144 (2000)Google Scholar
  22. 22.
    Lourakis, M.: levmar: Levenberg-Marquardt nonlinear least squares algorithms in C/C++. http://www.ics.forth.gr/~lourakis/levmar/
  23. 23.
    Lourakis, M., Argyros, A.: SBA: a software package for generic sparse bundle adjustment. ACM Trans. Math. Softw. 36(1), 1–30 (2009). http://www.ics.forth.gr/~lourakis/sba/
  24. 24.
    Nakamura, Y., Matsuura, T., Satoh, K., Ohta, Y.: Occlusion detectable stereo - occlusion patterns in camera matrix. In: CVPR, pp. 371–378 (1996)Google Scholar
  25. 25.
    Posdamer, J., Altschuler, M.: Surface measurement by space-encoded projected beam systems. CGIP 18(1), 1–17 (1982)Google Scholar
  26. 26.
    Salvi, J., Fernandez, S., Pribanic, T., Llado, X.: A state of the art in structured light patterns for surface profilometry. Pat. Rec. 43(8), 2666–2680 (2010)CrossRefzbMATHGoogle Scholar
  27. 27.
    Scharstein, D.: Middlebury stereo page. http://vision.middlebury.edu/stereo/
  28. 28.
    Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. IJCV 47(1–3), 7–42 (2002)CrossRefzbMATHGoogle Scholar
  29. 29.
    Scharstein, D., Szeliski, R.: High-accuracy stereo depth maps using structured light. In: CVPR, vol. I, pp. 195–202 (2003)Google Scholar
  30. 30.
    Seitz, S., Curless, B., Diebel, J., Scharstein, D., Szeliski, R.: A comparison and evaluation of multi-view stereo reconstruction algorithms. In: CVPR, vol. 1, pp. 519–528 (2006)Google Scholar
  31. 31.
    Strecha, C., von Hansen, W., Van Gool, L., Fua, P., Thoennessen, U.: On benchmarking camera calibration and multi-view stereo for high resolution imagery. In: CVPR, pp. 1–8 (2008)Google Scholar
  32. 32.
    Will, P., Pennington, K.: Grid coding: A preprocessing technique for robot and machine vision. In: IJCAI, pp. 66–70 (1971)Google Scholar
  33. 33.
    Zabih, R., Woodfill, J.: Non-parametric local transforms for computing visual correspondence. In: ECCV, pp. 151–158 (1994)Google Scholar
  34. 34.
    Zhang, L., Curless, B., Seitz, S.: Spacetime stereo: shape recovery for dynamic scenes. In: CVPR, vol. II, pp. 367–374 (2003)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Daniel Scharstein
    • 1
    Email author
  • Heiko Hirschmüller
    • 2
  • York Kitajima
    • 1
  • Greg Krathwohl
    • 1
  • Nera Nešić
    • 3
  • Xi Wang
    • 1
  • Porter Westling
    • 4
  1. 1.Middlebury CollegeMiddleburyUSA
  2. 2.German Aerospace CenterOberpfaffenhofenGermany
  3. 3.Reykjavik UniversityReykjavikIceland
  4. 4.LiveRampSan FranciscoUSA

Personalised recommendations