High-Resolution Stereo Datasets with Subpixel-Accurate Ground Truth

  • Daniel Scharstein
  • 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.

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)MATHGoogle 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)CrossRefMATHGoogle 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)CrossRefMATHGoogle 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)CrossRefMATHGoogle 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
  • 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

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