ECCV 2016: Computer Vision – ECCV 2016 pp 170-186 | Cite as

Photometric Stereo Under Non-uniform Light Intensities and Exposures

  • Donghyeon Cho
  • Yasuyuki Matsushita
  • Yu-Wing Tai
  • Inso Kweon
Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9906)

Abstract

This paper studies the effects of non-uniform light intensities and sensor exposures across observed images in photometric stereo. While conventional photometric stereo methods typically assume that light intensities are identical and sensor exposure is constant across observed images taken under varying lightings, these assumptions easily break down in practical settings due to individual light bulb’s characteristics and limited control over sensors. Our method explicitly models these non-uniformities and develops a method for accurately determining surface normal without affected by these factors. In addition, we show that our method is advantageous for general photometric stereo settings, where auto-exposure control is desirable. We compare our method with conventional least-squares and robust photometric stereo methods, and the experimental result shows superior accuracy of our method in this practical circumstance.

Keywords

Photometric stereo Shape estimation Unknown light intensity and exposure Surface normal 
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Notes

Acknowledgement

This work is partly supported by JSPS KAKENHI Grant Number JP16H01732 and the Ministry of Trade, Industry & Energy and the Korea Evaluation Institute of Industrial Technology (KEIT) with the program number of 10060110.

References

  1. 1.
    Woodham, R.J.: Photometric method for determining surface orientation from multiple images. Opt. Eng. 19(1), 139–144 (1980)CrossRefGoogle Scholar
  2. 2.
    Wu, T., Tang, K., Tang, C., Wong, T.: Dense photometric stereo: a Markov random field approach. IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI) 28(11), 1830–1846 (2006)CrossRefGoogle Scholar
  3. 3.
    Verbiest, F., Van Gool, L.: Photometric stereo with coherent outlier handling and confidence estimation. In: Proceedings of Computer Vision and Pattern Recognition (CVPR), pp. 2886–2893 (2008)Google Scholar
  4. 4.
    Wu, L., Ganesh, A., Shi, B., Matsushita, Y., Wang, Y., Ma, Y.: Robust photometric stereo via low-rank matrix completion and recovery. In: Kimmel, R., Klette, R., Sugimoto, A. (eds.) ACCV 2010. LNCS, vol. 6494, pp. 703–717. Springer, Heidelberg (2011). doi: 10.1007/978-3-642-19318-7_55 CrossRefGoogle Scholar
  5. 5.
    Oh, T.H., Kim, H., Tai, Y.W., Bazin, J.C., Kweon, I.S.: Partial sum minimization of singular values in RPCA for low-level vision. In: Proceedings of International Conference on Computer Vision (ICCV), pp. 145–152 (2013)Google Scholar
  6. 6.
    Zhang, Y., Mu, C., Kuo, H.W., Wright, J.: Toward guaranteed illumination models for non-covex objects. In: Proceedings of International Conference on Computer Vision (ICCV), pp. 937–944 (2013)Google Scholar
  7. 7.
    Hayakawa, H.: Photometric stereo under a light-source with arbitrary motion. J. Opt. Soc. Am. (JOSA) 11(11), 3078–3089 (1994)MathSciNetGoogle Scholar
  8. 8.
    Yuille, A.L., Snow, D.: Shape and albedo from multiple images using integrability. In: Proceedings of Computer Vision and Pattern Recognition (CVPR), pp. 158–164 (1997)Google Scholar
  9. 9.
    Belhumeur, P.N., Kriegman, D.J., Yuille, A.L.: The bas-relief ambiguity. Int. J. Comput. Vis. (IJCV) 35(1), 33–44 (1999)CrossRefGoogle Scholar
  10. 10.
    Alldrin, N.G., Mallick, S.P., Kriegman, D.J.: Resolving the generalized bas-relief ambiguity by entropy minimization. In: Proceedings of Computer Vision and Pattern Recognition (CVPR), pp. 1–7 (2007)Google Scholar
  11. 11.
    Drbohlav, O., Chantler, M.: Can two specular pixels calibrate photometric stereo? In: Proceedings of International Conference on Computer Vision (ICCV), pp. 1850–1857 (2005)Google Scholar
  12. 12.
    Sunkavalli, K., Zickler, T., Pfister, H.: Visibility subspaces: uncalibrated photometric stereo with shadows. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010. LNCS, vol. 6312, pp. 251–264. Springer, Heidelberg (2010). doi: 10.1007/978-3-642-15552-9_19 CrossRefGoogle Scholar
  13. 13.
    Shi, B., Matsushita, Y., Wei, Y., Xu, C., Tan, P.: Self-calibrating photometric stereo. In: Proceedings of Computer Vision and Pattern Recognition (CVPR), pp. 1118–1125 (2010)Google Scholar
  14. 14.
    Solomon, F., Ikeuchi, K.: Extracting the shape and roughness of specular lobe objects using four light photometric stereo. IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI) 18(4), 449–454 (1996)CrossRefGoogle Scholar
  15. 15.
    Georghiades, A.: Incorporating the torrance and sparrow model of reflectance in uncalibrated photometric stereo. In: Proceedings of International Conference on Computer Vision (ICCV), pp. 816–823 (2003)Google Scholar
  16. 16.
    Lin, S., Lee, S.W.: Estimation of diffuse and specular appearance. In: Proceedings of International Conference on Computer Vision (ICCV), pp. 855–860 (1999)Google Scholar
  17. 17.
    Iwahori, Y., Woodham, R.J., H.T., Ishii, N.: Neural network to reconstruct specular surface shape from its three shading images. In: Proceedings of International Joint Conference on Neural Networks, pp. 1181–1184 (1993)Google Scholar
  18. 18.
    Malzbender, T., Wilburn, B., Gelb, D., Ambrisco, B.: Surface enhancement using real-time photometric stereo and reflectance transformation. In: Proceedings of the Eurographics Symposium on Rendering Techniques, pp. 245–250 (2006)Google Scholar
  19. 19.
    Shi, B., Tan, P., Matsushita, Y., Ikeuchi, K.: Bi-polynomial modeling of low-frequency reflectances. IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI) 36(6), 1078–1091 (2014)CrossRefGoogle Scholar
  20. 20.
    Ikehata, S., Wipf, D.P., Matsushita, Y., Aizawa, K.: Photometric stereo using sparse Bayesian regression for general diffuse surfaces. IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI) 36(9), 1816–1831 (2014)CrossRefGoogle Scholar
  21. 21.
    Sylvester, J.: Sur lequations en matrices px = xq. C. R. Acad. Sci. Paris 99(2), 67–71,115–116 (1884)Google Scholar
  22. 22.
    Holland, P.W., Welsch, R.E.: Robust regression using iteratively reweighted least-squares. Commun. Stat. Theor. Methods 6(9), 813–827 (1977)CrossRefMATHGoogle Scholar
  23. 23.
    Kamgar-Parsi, B., Kamgar-Parsi, B.: Evaluation of quantization error in computer vision. IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI) 11(9), 929–940 (1989)CrossRefMATHGoogle Scholar
  24. 24.
    Ikehata, S., Aizawa, K.: Photometric stereo using constrained bivariate regression for general isotropic surfaces. In: Proceedings of Computer Vision and Pattern Recognition (CVPR), pp. 2187–2194 (2014)Google Scholar
  25. 25.
    Xie, W., Zhang, Y., Wang, C.C.L., Chung, R.C.K.: Surface-from-gradients: an approach based on discrete geometry processing. In: Proceedings of Computer Vision and Pattern Recognition (CVPR), pp. 2203–2210 (2014)Google Scholar
  26. 26.
    Lee, J.Y., Matsushita, Y., Shi, B., Kweon, I.S., Ikeuchi, K.: Radiometric calibration by rank minimization. IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI) 35(1), 144–156 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Donghyeon Cho
    • 1
  • Yasuyuki Matsushita
    • 2
  • Yu-Wing Tai
    • 3
  • Inso Kweon
    • 1
  1. 1.KAISTDaejeonSouth Korea
  2. 2.Osaka UniversitySuitaJapan
  3. 3.SenseTime Group LimitedBeijingChina

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