Advertisement

Light Structure from Pin Motion: Simple and Accurate Point Light Calibration for Physics-Based Modeling

  • Hiroaki Santo
  • Michael Waechter
  • Masaki Samejima
  • Yusuke Sugano
  • Yasuyuki Matsushita
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11207)

Abstract

We present a practical method for geometric point light source calibration. Unlike in prior works that use Lambertian spheres, mirror spheres, or mirror planes, our calibration target consists of a Lambertian plane and small shadow casters at unknown positions above the plane. Due to their small size, the casters’ shadows can be localized more precisely than highlights on mirrors. We show that, given shadow observations from a moving calibration target and a fixed camera, the shadow caster positions and the light position or direction can be simultaneously recovered in a structure from motion framework. Our evaluation on simulated and real scenes shows that our method yields light estimates that are stable and more accurate than existing techniques while having a considerably simpler setup and requiring less manual labor.

This project’s source code can be downloaded from: https://github.com/hiroaki-santo/light-structure-from-pin-motion.

Keywords

Light source calibration Photometric stereo Shape-from-shading Appearance modeling Physics-based modeling 

Notes

Acknowledgments

This work was supported by JSPS KAKENHI Grant Number JP16H01732. Michael Waechter is grateful for support through a postdoctoral fellowship by the Japan Society for the Promotion of Science.

References

  1. 1.
    Silver, W.M.: Determining shape and reflectance using multiple images. Master’s thesis, Massachusetts Institute of Technology (1980)Google Scholar
  2. 2.
    Woodham, R.J.: Photometric method for determining surface orientation from multiple images. Opt. Eng. 19(1), 139–144 (1980)CrossRefGoogle Scholar
  3. 3.
    Hu, B., Brown, C.M., Nelson, R.C.: The geometry of point light source from shadows. Technical report UR CSD/TR810, University of Rochester (2004)Google Scholar
  4. 4.
    Shen, H.L., Cheng, Y.: Calibrating light sources by using a planar mirror. J. Electron. Imaging 20(1), 013002-1–013002-6 (2011)CrossRefGoogle Scholar
  5. 5.
    Zhang, Y., Yang, Y.H.: Multiple illuminant direction detection with application to image synthesis. IEEE Trans. Pattern Anal. Mach. Intell. (PAMI) 23(8), 915–920 (2001)CrossRefGoogle Scholar
  6. 6.
    Wei, J.: Robust recovery of multiple light source based on local light source constant constraint. Pattern Recogn. Lett. 24(1), 159–172 (2003)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Wang, Y., Samaras, D.: Estimation of multiple directional light sources for synthesis of mixed reality images. In: Proceedings of the Pacific Conference on Computer Graphics and Applications, pp. 38–47 (2002)Google Scholar
  8. 8.
    Zhou, W., Kambhamettu, C.: Estimation of illuminant direction and intensity of multiple light sources. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2353, pp. 206–220. Springer, Heidelberg (2002).  https://doi.org/10.1007/3-540-47979-1_14CrossRefGoogle Scholar
  9. 9.
    Cao, X., Shah, M.: Camera calibration and light source estimation from images with shadows. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 918–923 (2005)Google Scholar
  10. 10.
    Sato, I., Sato, Y., Ikeuchi, K.: Stability issues in recovering illumination distribution from brightness in shadows. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. II-400-II-407 (2001)Google Scholar
  11. 11.
    Sato, I., Sato, Y., Ikeuchi, K.: Illumination from shadows. IEEE Trans. Pattern Anal. Mach. Intell. (PAMI) 25(3), 290–300 (2003)CrossRefGoogle Scholar
  12. 12.
    Powell, M.W., Sarkar, S., Goldgof, D.: A simple strategy for calibrating the geometry of light sources. IEEE Trans. Pattern Anal. Mach. Intell. (PAMI) 23(9), 1022–1027 (2001)CrossRefGoogle Scholar
  13. 13.
    Hara, K., Nishino, K., Ikeuchi, K.: Light source position and reflectance estimation from a single view without the distant illumination assumption. IEEE Trans. Pattern Anal. Mach. Intell. (PAMI) 27(4), 493–505 (2005)CrossRefGoogle Scholar
  14. 14.
    Wong, K.-Y.K., Schnieders, D., Li, S.: Recovering light directions and camera poses from a single sphere. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008. LNCS, vol. 5302, pp. 631–642. Springer, Heidelberg (2008).  https://doi.org/10.1007/978-3-540-88682-2_48CrossRefGoogle Scholar
  15. 15.
    Takai, T., Maki, A., Niinuma, K., Matsuyama, T.: Difference sphere: an approach to near light source estimation. Comput. Vis. Image Underst. J. (CVIU) 113(9), 966–978 (2009)CrossRefGoogle Scholar
  16. 16.
    Schnieders, D., Wong, K.Y.K.: Camera and light calibration from reflections on a sphere. Comput. Vis. Image Underst. J. (CVIU) 117(10), 1536–1547 (2013)CrossRefGoogle Scholar
  17. 17.
    Weber, M., Cipolla, R.: A practical method for estimation of point light-sources. In: Proceedings of the British Machine Vision Conference (BMVC), vol. 2, pp. 471–480 (2001)Google Scholar
  18. 18.
    Aoto, T., Taketomi, T., Sato, T., Mukaigawa, Y., Yokoya, N.: Position estimation of near point light sources using a clear hollow sphere. In: Proceedings of the International Conference on Pattern Recognition (ICPR), pp. 3721–3724 (2012)Google Scholar
  19. 19.
    Bunteong, A., Chotikakamthorn, N.: Light source estimation using feature points from specular highlights and cast shadows. Int. J. Phys. Sci. 11(13), 168–177 (2016)CrossRefGoogle Scholar
  20. 20.
    Ackermann, J., Fuhrmann, S., Goesele, M.: Geometric point light source calibration. In: Proceedings of Vision, Modeling, and Visualization, pp. 161–168 (2013)Google Scholar
  21. 21.
    Park, J., Sinha, S.N., Matsushita, Y., Tai, Y., Kweon, I.: Calibrating a non-isotropic near point light source using a plane. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2267–2274 (2014)Google Scholar
  22. 22.
    Schnieders, D., Wong, K.-Y.K., Dai, Z.: Polygonal light source estimation. In: Zha, H., Taniguchi, R., Maybank, S. (eds.) ACCV 2009. LNCS, vol. 5996, pp. 96–107. Springer, Heidelberg (2010).  https://doi.org/10.1007/978-3-642-12297-2_10CrossRefGoogle Scholar
  23. 23.
    Snavely, N., Seitz, S.M., Szeliski, R.: Photo tourism: exploring photo collections in 3D. In: Proceedings of SIGGRAPH, pp. 835–846 (2006)Google Scholar
  24. 24.
    Triggs, B., McLauchlan, P.F., Hartley, R.I., Fitzgibbon, A.W.: Bundle adjustment—a modern synthesis. In: Triggs, B., Zisserman, A., Szeliski, R. (eds.) IWVA 1999. LNCS, vol. 1883, pp. 298–372. Springer, Heidelberg (2000).  https://doi.org/10.1007/3-540-44480-7_21CrossRefGoogle Scholar
  25. 25.
    Szeliski, R.: Computer Vision: Algorithms and Applications. Springer, Heidelberg (2010).  https://doi.org/10.1007/978-1-84882-935-0CrossRefzbMATHGoogle Scholar
  26. 26.
    Hartley, R.I., Sturm, P.: Triangulation. Comput. Vis. Image Underst. J. (CVIU) 68(2), 146–157 (1997)CrossRefGoogle Scholar
  27. 27.
    Nocedal, J., Wright, S.J.: Numerical Optimization. Springer, Heidelberg (2006).  https://doi.org/10.1007/978-0-387-40065-5CrossRefzbMATHGoogle Scholar
  28. 28.
    Garrido-Jurado, S., Muñoz-Salinas, R., Madrid-Cuevas, F.J., Marín-Jiménez, M.J.: Automatic generation and detection of highly reliable fiducial markers under occlusion. Pattern Recognit. 47(6), 2280–2292 (2014)CrossRefGoogle Scholar
  29. 29.
    Bradski, G.: The OpenCV library. Dr. Dobb’s J. Softw. Tools (2000). https://github.com/opencv/opencv/wiki/CiteOpenCV

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Hiroaki Santo
    • 1
  • Michael Waechter
    • 1
  • Masaki Samejima
    • 1
  • Yusuke Sugano
    • 1
  • Yasuyuki Matsushita
    • 1
  1. 1.Osaka UniversityOsakaJapan

Personalised recommendations