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Shape in a Box

  • Graham D. Finlayson
  • Christopher PowellEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8927)

Abstract

Many techniques have been developed in computer vision to recover three-dimensional shape from two-dimensional images. These techniques impose various combinations of assumptions/restrictions of conditions to produce a representation of shape (e.g. a depth/height map). Although great progress has been made it is a problem which remains far from solved, with most methods requiring a non-passive imaging environment. In this paper we develop on a variant of photometric stereo called “Shape from color” (SFC). We remove the restriction of known, direct light sources by exploiting mutual illumination; we simply take pictures of objects within a colourful box, hence “Shape in a Box”. We discuss the engineering process used to develop our set-up and demonstrate experimentally that our passive imaging environment recovers shape to the same accuracy as SFC. A second contribution of this paper is to benchmark our approach using real objects with known ground truth, including some 3D printed objects.

Keywords

Photometric stereo Mutual illumination Shape recovery 

References

  1. 1.
    Barron, J.T., Malik, J.: Shape, albedo, and illumination from a single image of an unknown object. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 334–341 (2012)Google Scholar
  2. 2.
    Barron, J.T., Malik, J.: Shape, illumination, and reflectance from shading. Tech. rep, UC Berkeley (2013)Google Scholar
  3. 3.
    Barsky, S., Petrou, M.: The 4-source photometric stereo technique for three-dimensional surfaces in the presence of highlights and shadows. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(10), 1239–1252 (2003)CrossRefGoogle Scholar
  4. 4.
    Barsky, S., Petrou, M.: Design issues for a colour photometric stereo system. Journal of Mathematical Imaging and Vision 24(1), 143–162 (2006)CrossRefMathSciNetGoogle Scholar
  5. 5.
    Brostow, G.J., Hernández, C., Vogiatzis, G., Stenger, B., Cipolla, R.: Video normals from colored lights. IEEE Transactions on Pattern Analysis and Machine Intelligence 33(10), 2104–2114 (2011)CrossRefGoogle Scholar
  6. 6.
    Brown, M.Z., Burschka, D., Hager, G.D.: Advances in computational stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(8), 993–1008 (2003)CrossRefGoogle Scholar
  7. 7.
    Debevec, P.E., Malik, J.: Recovering high dynamic range radiance maps from photographs. Proceedings of SIGGRAPH 2007, 369–378 (2007)Google Scholar
  8. 8.
    Dhond, U.R., Aggarwal, J.K.: Structure from stereo-a review. IEEE Transactions on Systems Man and Cybernetics 19(6), 1489–1510 (1989)CrossRefMathSciNetGoogle Scholar
  9. 9.
    Drew, M.S.: Shape from color. Simon Fraser University, Tech. rep. (1992)Google Scholar
  10. 10.
    Drew, M.S.: Photometric stereo without multiple images. Electronic Imaging 1997, 369–380 (1997)Google Scholar
  11. 11.
    Drew, M.S., Brill, M.H.: Color from shape from color: A simple formalism with known light sources. Journal of the Optical Society of America (JOSA) 17(8), 1371–1381 (2000)CrossRefGoogle Scholar
  12. 12.
    Durou, J.D., Falcone, M., Sagona, M.: Numerical methods for shape-from-shading: A new survey with benchmarks. Computer Vision and Image Understanding 109(1), 22–43 (2008)CrossRefGoogle Scholar
  13. 13.
    Frankot, R.T., Chellappa, R.: A method for enforcing integrability in shape from shading algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence 10(4), 439–451 (1988)CrossRefzbMATHGoogle Scholar
  14. 14.
    Fyffe, G., Yu, X., Debevec, P.: Single-shot photometric stereo by spectral multiplexing. In: 2011 IEEE International Conference on Computational Photography (ICCP), pp. 1–6 (2011)Google Scholar
  15. 15.
    Green, R.: Spherical harmonic lighting: the gritty details. In: Archives of the Game Developers Conference (2003)Google Scholar
  16. 16.
    Hernández, C., Vogiatzis, G., Brostow, G.J., Stenger, B., Cipolla, R.: Non-rigid photometric stereo with colored lights. In: IEEE 11th International Conference on Computer Vision (ICCV), pp. 1–8 (2007)Google Scholar
  17. 17.
    Hernández, C., Vogiatzis, G., Cipolla, R.: Shadows in three-source photometric stereo. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part I. LNCS, vol. 5302, pp. 290–303. Springer, Heidelberg (2008) CrossRefGoogle Scholar
  18. 18.
    Horn, B.K.: Shape from shading: A method for obtraining the shape of a smooth opaque object from one view. Tech. rep, Massachusetts Institute of Technology (1970)Google Scholar
  19. 19.
    Horn, B.K.: Understanding image intensities. Artificial Intelligence 8(2), 201–231 (1977)CrossRefzbMATHGoogle Scholar
  20. 20.
    Johnson, M.K., Adelson, E.H.: Shape estimation in natural illumination. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2553–2560 (2011)Google Scholar
  21. 21.
    Kirk, K., Andersen, H.J.: Noise characterization of weighting schemes for combination of multiple exposures. In: British Machine Vision Conference (BMVC), pp. 1129–1138 (2006)Google Scholar
  22. 22.
    Klaudiny, M., Hilton, A.: Error analysis of photometric stereo with colour lights. Pattern Recognition Letters (2014). http://www.sciencedirect.com/science/article/pii/S0167865513005114
  23. 23.
    Kovesi, P.: Shapelets correlated with surface normals produce surfaces. In: Tenth IEEE International Conference on Computer Vision (ICCV), vol. 2, pp. 994–1001 (2005)Google Scholar
  24. 24.
    Patel, V.M., Chellappa, R.: Approximation methods for the recovery of shapes and images from gradients. Applied and Numerical Harmonic Analysis, 377–398 (2013)Google Scholar
  25. 25.
    Pérez, P., Gangnet, M., Blake, A.: Poisson image editing. ACM Transactions on Graphics (TOG) 22(3), 313–318 (2003)CrossRefGoogle Scholar
  26. 26.
    Ramamoorthi, R., Hanrahan, P.: An efficient representation for irradiance environment maps. Proceedings of SIGGRAPH 2001, 497–500 (2001)Google Scholar
  27. 27.
    Schönefeld, V.: Spherical harmonics. RWTH Aachen University, Tech. rep. (2005)Google Scholar
  28. 28.
    Simchony, T., Chellappa, R., Shao, M.: Direct analytical methods for solving poisson equations in computer vision problems. IEEE Transactions on Pattern Analysis and Machine Intelligence 12(5), 435–446 (1990)CrossRefGoogle Scholar
  29. 29.
    Sun, J., Smith, M., Smith, L., Farooq, A.: Examining the uncertainty of the recovered surface normal in three light photometric stereo. Image and Vision Computing 25(7), 1073–1079 (2007)CrossRefGoogle Scholar
  30. 30.
    Vogiatzis, G., Hernández, C.: Self-calibrated, multi-spectral photometric stereo for 3d face capture. International Journal of Computer Vision 97(1), 91–103 (2012)CrossRefGoogle Scholar
  31. 31.
    Weng, J., Huang, T.S., Ahuja, N.: Motion and structure from image sequences. Springer Publishing Company (2012)Google Scholar
  32. 32.
    Woodham, R.J.: Photometric method for determining surface orientation from multiple images. Optical Engineering 19(1), 139–144 (1980)CrossRefGoogle Scholar
  33. 33.
    Zhang, R., Tsai, P.S., Cryer, J.E., Shah, M.: Shape-from-shading: a survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 21(8), 690–706 (1999)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.School of Computing SciencesUniversity of East AngliaNorwichUK

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