3D Reconstruction of Interreflection-affected Surface Concavities using Photometric Stereo

Steffen Herbort, Daniel Schugk, Christian Wöhler

Abstract

Image-based reconstruction of 3D shapes is inherently biased under the occurrence of interreflections, since the observed intensity at surface concavities consists of direct and global illumination components. This issue is commonly not considered in a Photometric Stereo (PS) framework. Under the usual assumption of only direct reflections, this corrupts the normal estimation process in concave regions and thus leads to inaccurate results. For this reason, global illumination effects need to be considered for the correct reconstruction of surfaces affected by interreflections. While there is ongoing research in the field of inverse lighting (i.e. separation of global and direct illumination components), the interreflection aspect remains oftentimes neglected in the field of 3D shape reconstruction. In this study, we present a computationally driven approach for iteratively solving that problem. Initially, we introduce a photometric stereo approach that roughly reconstructs a surface with at first unknown reflectance properties. Then, we show that the initial surface reconstruction result can be refined iteratively regarding non-distant light sources and, especially, interreflections. The benefit for the reconstruction accuracy is evaluated on real Lambertian surfaces using laser range scanner data as ground truth.

References

  1. Agrawal, A., Raskar, R., and Chellappa, R. (2006). What is the range of surface reconstructions from a gradient field? Proceedings of the European Conference on Computer Vision (ECCV 2006), 1(TR2006-021):578- 591.
  2. Alldrin, N., Zickler, T., and Kriegman, D. (2008). Photometric stereo with non-parametric and spatiallyvarying reflectance. 2008 Conference on Computer Vision and Pattern Recognition (CVPR2008).
  3. Alldrin, N. G., Mallick, S. P., and Kriegman, D. J. (2007). Resolving the generalized bas-relief ambiguity by entropy minimization. 2007 Conference on Computer Vision and Pattern Recognition (CVPR).
  4. Basri, R., Jacobs, D. W., and Kemelmacher, I. (2007). Photometric stereo with general, unknown lighting. International Journal of Computer Vision (IJCV), 72(3):239-257.
  5. Beckmann, P. and Spizzichino, A. (1987). The Scattering of Electromagnetic Waves from Rough Surfaces. Number ISBN-13: 987-0890062382. Artech House Radar Library.
  6. Belhumeur, P. N., Kriegman, D. J., and Yuille, A. L. (1999). The bas-relief ambiguity. International Journal of Computer Vision (IJCV), 35(1):1040-1046.
  7. Blinn, J. F. (1977). Models of light reflection for computer synthesized pictures. ACM SIGGRAPH Computer Graphics, 11(2):192-198.
  8. Clark, J. J. (1992). Active photometric stereo. Proceedings of the 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'92), pages 29-34.
  9. Couture, V., Martin, N., and Roy, S. (2011). Unstructured light scanning to overcome interreflections. ICCV'2011, pages 1-8.
  10. Freeman, W. T. (1994). The generic viewpoint assumption in a framework for visual perception. Nature, 368:542-545.
  11. Geisler-Moroder, D. and Dür, A. (2010). A new ward brdf model with bounded albedo. Eurographics Symposium on Rendering 2010, 29:1391-1398.
  12. Gu, J., Kobayashi, T., Gupta, M., and Nayar, S. K. (2011). Multiplexed illumination for scene recovery in the presence of global illumination. ICCV 2011, pages 1-8.
  13. Gupta, M., Agrawal, A., Veeraraghavan, A., and Narasimhan, S. G. (2011). A practical approach to 3d scanning in the presence of interreflections, subsurface scattering and defocus. CVPR'2011, IJCV'2012, pages 1-24.
  14. Hayakawa, H. (1994). Photometric stereo under a light source with arbitrary motion. Journal of Optical Society of America A (JOSA A), 11:3079-3089.
  15. Horn, B. K. P. (1970). Shape from shading: A method for obtaining the shape of a smooth opaque object from one view. Technical Report 232, Messachusets Institute of Technology.
  16. Ikeuchi, K. (1981). Determining surface orientations of specular surfaces by using the photometric stereo method. IEEE Transactions on Pattern Analysis and Machine Intelligence, 3(6):661-669.
  17. Iwahori, Y., Sugie, H., and Ishii, N. (1990). Reconstructing shape from shading images under point light source illumination. Proceedings of IEEE 10th International Conference on Pattern Recognition (ICPR'90), 1:83- 87.
  18. Lafortune, E. P. F., Foo, S.-C., Torrance, K. E., and Greenberg, D. P. (1997). Non-linear approximation of reflectance functions. SIGGRAPH'97, pages 117-126.
  19. Lambert, J.-H. (1760). Photometria, sive de mensura et gradibus luminis, colorum et umbrae. Vidae Eberhardi Klett.
  20. Lenoch, M., Herbort, S., and Wöhler, C. (2012). Robust and accurate light source calibration using a diffuse spherical calibration object. Oldenburger 3D Tage 2012, 11:1-8.
  21. Nayar, S. K., Fang, X.-S., and Boult, T. (1997). Separation of reflection components using color and polarization. International Journal of Computer Vision, 21(3):163- 186.
  22. Nayar, S. K., Ikeuchi, K., and Kanade, T. (1988). Extracting shape and reflectance of lambertian, specular and hybrid surfaces. Technical Report CMU-FU-TR-88-14, The Robotics Institute, Carnegie Mellon University.
  23. Nayar, S. K., Ikeuchi, K., and Kanade, T. (1990a). Determining shape and reflectance of hybrid surfaces by photometric sampling. IEEE Transactions on Robotics and Automation, 6(1):418-431.
  24. Nayar, S. K., Ikeuchi, K., and Kanade, T. (1990b). Shape from interreflections. Technical Report CMU-RI-TR90-14, Carnegie-Mellon University of Pittsburgh, PA, Robotics Institute.
  25. Nayar, S. K., Krishnan, G., Grossberg, M. D., and Raskar, R. (2006). Fast separation of direct and global components of a scene using high frequency illumination. ACM Transactions on Graphics (TOG2006), Proceedings of ACM SIGGRAPH 2006, 25(3):935-944.
  26. Oren, M. and Nayar, S. K. (1994). Generalization of lambert's reflectance model. Proceedings of the 21st Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH 1994), pages 239-246.
  27. Pharr, M. and Humphreys, G. (2010). Physically Based Rendering - From Theory to Implementation. Morgan Kaufmann (Elsvier).
  28. Phong, B. T. (1975). Illumination for computer generated pictures. Communications of the ACM, 18(6):311 -17.
  29. Seitz, S. M., Matasushita, Y., and Kutulakos, K. N. (2005). A theory of inverse light transport. ICCV 2005, pages 1440 -- 1447.
  30. Tan, P. and Zickler, T. (2009). A projective framework for radiometric image analysis. CVPR 2009, pages 2977- 2984.
  31. Tan, R. T. and Ikeuchi, K. (2005). Separating reflection components of textured surfaces using a single image.
  32. PAMI'05, 27(2):179-193.
  33. Tankus, A., Sochen, N., and Yeshurun, Y. (2005). Shapefrom-shading under perspective projection. International Journal of Computer Vision, 63(1):21-43.
  34. Thomas, D. and Sugimoto, A. (2010). Range image registration of specular objects under complex illumination. Fifth International Symposium on 3D Data Processing, Visualization and Transmission (3DPVT2010).
  35. Torrance, K. E. and Sparrow, E. M. (1967). Theory for off-specular reflection from roughened surfaces. Journal of the Optical Society of America A (JOSA A), 57(9):1105-1114.
  36. Ward, G. J. (1992). Measuring and modeling anisotropic reflection. ACM SIGGRAPH Computer Graphics, 26(2):265-272.
  37. Wolff, L. B. (1989). Using polarization to separate reflection components. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'89), 1(1):363-369.
  38. Wolff, L. B. and Boult, T. E. (1991). Constraining object features using a polarization reflectance model. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13(7):635-657.
  39. Woodham, R. J. (1980). Photometric method for determining surface orientation from multiple images. Optical Engineering, 19(1):139-144.
  40. Yu, Y., Debevec, P., Malik, J., and Hawkins, T. (1999). Inverse global illumination: Recovering reflectance models of real scenes from photographs. Association for Computing Machinery, Special Interest Group on Computer Graphics and Interactive Techniques (ACM SIGGRAPH1999), pages 215-224.
  41. Yuille, A. L., Coughlan, J. M., and Konishi, S. (2000). The generic viewpoint constraint resolves the generalized bas relief ambiguity. Conference on Information Science and Systems.
  42. Zhou, Z. and Tan, P. (2010). Ring-light photometric stereo. Proceedings of the 11th European Conference on Computer Vision (ECCV'10), pages 1-14.
  43. Zickler, T., Belhumeur, P. N., and Kriegman, D. J. (2002). Helmholtz stereopsis: Exploiting reciprocity for surface reconstruction. Procedings of the European Conference on Computer Vision 2002, 3:869-884.
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Paper Citation


in Harvard Style

Herbort S., Schugk D. and Wöhler C. (2013). 3D Reconstruction of Interreflection-affected Surface Concavities using Photometric Stereo . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2013) ISBN 978-989-8565-48-8, pages 208-214. DOI: 10.5220/0004213702080214


in Bibtex Style

@conference{visapp13,
author={Steffen Herbort and Daniel Schugk and Christian Wöhler},
title={3D Reconstruction of Interreflection-affected Surface Concavities using Photometric Stereo},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2013)},
year={2013},
pages={208-214},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004213702080214},
isbn={978-989-8565-48-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2013)
TI - 3D Reconstruction of Interreflection-affected Surface Concavities using Photometric Stereo
SN - 978-989-8565-48-8
AU - Herbort S.
AU - Schugk D.
AU - Wöhler C.
PY - 2013
SP - 208
EP - 214
DO - 10.5220/0004213702080214