“Acquiring reflectance and shape from continuous spherical harmonic illumination” by Tunwattanapong, Fyffe, Graham, Busch, Yu, et al. …

  • ©Borom Tunwattanapong, Graham Fyffe, Paul Graham, Jay Busch, Xueming Yu, Abhijeet Ghosh, and Paul E. Debevec




    Acquiring reflectance and shape from continuous spherical harmonic illumination

Session/Category Title:   Materials




    We present a novel technique for acquiring the geometry and spatially-varying reflectance properties of 3D objects by observing them under continuous spherical harmonic illumination conditions. The technique is general enough to characterize either entirely specular or entirely diffuse materials, or any varying combination across the surface of the object. We employ a novel computational illumination setup consisting of a rotating arc of controllable LEDs which sweep out programmable spheres of incident illumination during 1-second exposures. We illuminate the object with a succession of spherical harmonic illumination conditions, as well as photographed environmental lighting for validation. From the response of the object to the harmonics, we can separate diffuse and specular reflections, estimate world-space diffuse and specular normals, and compute anisotropic roughness parameters for each view of the object. We then use the maps of both diffuse and specular reflectance to form correspondences in a multiview stereo algorithm, which allows even highly specular surfaces to be corresponded across views. The algorithm yields a complete 3D model and a set of merged reflectance maps. We use this technique to digitize the shape and reflectance of a variety of objects difficult to acquire with other techniques and present validation renderings which match well to photographs in similar lighting.


    1. Adato, Y., Vasilyev, Y., Ben-Shahar, O., and Zickler, T. 2007. Toward a theory of shape from specular flow. In Proc. IEEE International Conference on Computer Vision, 1–8.Google Scholar
    2. Blake, A., and Brelstaff, G. 1992. In Physics-Based Vision, Principles and Practice: Shape Recovery, L. B. Wolff, S. A. Shafer, and G. E. Healey, Eds. Jones and Bartlett Publishers, Inc., USA, ch. Geometry from specularities, 277–286. Google ScholarDigital Library
    3. Bonfort, T., and Sturm, P. 2003. Voxel carving for specular surfaces. In Proc. IEEE International Conference on Computer Vision, 591–596. Google ScholarDigital Library
    4. Chen, T., Goesele, M., and Seidel, H. P. 2006. Mesostructure from specularities. In CVPR, 1825–1832. Google ScholarDigital Library
    5. Dana, K. J., van Ginneken, B., Nayar, S. K., and Koenderink, J. J. 1999. Reflectance and texture of real-world surfaces. ACM Trans. Graph. 18, 1 (Jan.), 1–34. Google ScholarDigital Library
    6. Debevec, P., Hawkins, T., Tchou, C., Duiker, H.-P., Sarokin, W., and Sagar, M. 2000. Acquiring the reflectance field of a human face. In Proceedings of ACM SIGGRAPH 2000, 145–156. Google ScholarDigital Library
    7. Dong, Y., Wang, J., Tong, X., Snyder, J., Lan, Y., Ben-Ezra, M., and Guo, B. 2010. Manifold bootstrapping for svbrdf capture. ACM Trans. Graph. 29 (July), 98:1–98:10. Google ScholarDigital Library
    8. Francken, Y., Cuypers, T., Mertens, T., Gielis, J., and Bekaert, P. 2008. High quality mesostructure acquisition using specularities. CVPR, 1–7.Google Scholar
    9. Furukawa, Y., and Ponce, J. 2009. Dense 3D motion capture for human faces. In Proc. of CVPR 09.Google Scholar
    10. Gardner, A., Tchou, C., Hawkins, T., and Debevec, P. 2003. Linear light source reflectometry. In ACM TOG, 749–758. Google ScholarDigital Library
    11. Ghosh, A., Chen, T., Peers, P., Wilson, C. A., and Debevec, P. E. 2009. Estimating specular roughness and anisotropy from second order spherical gradient illumination. Comput. Graph. Forum 28, 4, 1161–1170. Google ScholarDigital Library
    12. Ghosh, A., Heidrich, W., Achutha, S., and O’Toole, M. 2010. A basis illumination approach to brdf measurement. Int. J. Comput. Vision 90, 2 (Nov.), 183–197. Google ScholarDigital Library
    13. Harris, C., and Stephens, M. 1988. A combined corner and edge detector. In Proc. of Fourth Alvey Vision Conference, 147–151.Google Scholar
    14. Hawkins, T., Einarsson, P., and Debevec, P. 2005. A dual light stage. In Proc. EGSR, 91–98. Google ScholarDigital Library
    15. Holroyd, M., Lawrence, J., Humphreys, G., and Zickler, T. 2008. A photometric approach for estimating normals and tangents. ACM Trans. Graph. 27, 5 (Dec.), 133:1–133:9. Google ScholarDigital Library
    16. Holroyd, M., Lawrence, J., and Zickler, T. 2010. A coaxial optical scanner for synchronous acquisition of 3d geometry and surface reflectance. ACM Trans. Graph. 29, 4 (July), 99:1–99:12. Google ScholarDigital Library
    17. Ihrke, I., Kutulakos, K. N., Lensch, H. P. A., Magnor, M., and Heidrich, W. 2010. Transparent and specular object reconstruction. Computer Graphics Forum 29, 8, 2400–2426.Google ScholarCross Ref
    18. Ikeuchi, K. 1981. Determining surface orientations of specular surfaces by using the photometric stereo method. IEEE Trans. Pattern Anal. Mach. Intell. 3, 6 (June), 661–669. Google ScholarDigital Library
    19. Kolmogorov, V. 2006. Convergent tree-reweighted message passing for energy minimization. IEEE Trans. Pattern Anal. Mach. Intell. 28 (October), 1568–1583. Google ScholarDigital Library
    20. Lamond, B., Peers, P., Ghosh, A., and Debevec, P. 2009. Image-based separation of diffuse and specular reflections using environmental structured illumination. In Proc. IEEE International Conf. Computational Photography.Google Scholar
    21. Lensch, H. P. A., Kautz, J., Goesele, M., Heidrich, W., and Seidel, H.-P. 2003. Image-based reconstruction of spatial appearance and geometric detail. ACM TOG 22, 2, 234–257. Google ScholarDigital Library
    22. Ma, W.-C., Hawkins, T., Peers, P., Chabert, C.-F., Weiss, M., and Debevec, P. 2007. Rapid acquisition of specular and diffuse normal maps from polarized spherical gradient illumination. In Rendering Techniques, 183–194. Google ScholarDigital Library
    23. Mcallister, D. K. 2002. A generalized surface appearance representation for computer graphics. PhD thesis, The University of North Carolina at Chapel Hill. AAI3061704. Google ScholarDigital Library
    24. Moré, J. J., Sorensen, D. C., Hillstrom, K. E., and Garbow, B. S. 1984. The MINPACK project. In Sources and Development of Mathematical Software, 88–111.Google Scholar
    25. Nayar, S., Ikeuchi, K., and Kanade, T. 1990. Determining shape and reflectance of hybrid surfaces by photometric sampling. IEEE Trans. Robotics and Automation 6, 4, 418–431.Google ScholarCross Ref
    26. Park, M., Kashyap, S., Collins, R., and Liu, Y. 2010. Data driven mean-shift belief propagation for non-gaussian mrfs. In Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on, 3547–3554.Google Scholar
    27. Ramamoorthi, R., and Hanrahan, P. 2001. An efficient representation for irradiance environment maps. In Proc. of ACM SIGGRAPH ’01, 497–500. Google ScholarDigital Library
    28. Ren, P., Wang, J., Snyder, J., Tong, X., and Guo, B. 2011. Pocket reflectometry. ACM Trans. Graph. 30, 4 (July), 45:1–45:10. Google ScholarDigital Library
    29. Sato, Y., Wheeler, M. D., and Ikeuchi, K. 1997. Object shape and reflectance modeling from observation. In Proceedings of the 24th annual conference on Computer graphics and interactive techniques, ACM Press/Addison-Wesley Publishing Co., New York, NY, USA, SIGGRAPH ’97, 379–387. Google ScholarDigital Library
    30. Sloan, P.-P., 2008. Stupid spherical harmonics (sh) tricks. Game Developer’s Conference, Feb. http://www.ppsloan.org/publications/.Google Scholar
    31. Tarini, M., Lensch, H. P., Goesele, M., and Seidel, H.-P. 2005. 3D acquisition of mirroring objects using striped patterns. Graphical Models 67, 4, 233–259. Google ScholarDigital Library
    32. Wang, C.-P., Snavely, N., and Marschner, S. 2011. Estimating dual-scale properties of glossy surfaces from step-edge lighting. ACM Trans. Graph. (Proc. SIGGRAPH Asia) 30, 6. Google ScholarDigital Library
    33. Ward, G. J. 1992. Measuring and modeling anisotropic reflection. SIGGRAPH Comput. Graph. 26, 2, 265–272. Google ScholarDigital Library
    34. Westin, S. H., Arvo, J. R., and Torrance, K. E. 1992. Predicting reflectance functions from complex surfaces. SIGGRAPH Comput. Graph. 26, 2 (July), 255–264. Google ScholarDigital Library
    35. Weyrich, T., Lawrence, J., Lensch, H. P. A., Rusinkiewicz, S., and, T. 2009. Principles of appearance acquisition and representation. Found. Trends. Comput. Graph. Vis. 4, 2 (Feb.), 75–191. Google ScholarDigital Library
    36. Woodham, R. J. 1980. Photometric method for determining surface orientation from multiple images. Optical Engineering 19, 1, 139–144.Google ScholarCross Ref
    37. Zickler, T. E., Belhumeur, P. N., and Kriegman, D. J. 2002. Helmholtz stereopsis: Exploiting reciprocity for surface reconstruction. Int. J. Comput. Vision 49, 2-3, 215–227. Google ScholarDigital Library
    38. Zickler, T., Ramamoorthi, R., Enrique, S., and Belhumeur, P. N. 2006. Reflectance sharing: Predicting appearance from a sparse set of images of a known shape. PAMI 28, 8, 1287–1302. Google ScholarDigital Library

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