SciELO - Scientific Electronic Library Online

vol.19 número2Morphological Filtering Algorithm for Restoring Images Contaminated by Impulse NoiseCamera as Position Sensor for a Ball and Beam Control System índice de autoresíndice de materiabúsqueda de artículos
Home Pagelista alfabética de revistas  

Servicios Personalizados




Links relacionados

  • No hay artículos similaresSimilares en SciELO


Computación y Sistemas

versión impresa ISSN 1405-5546

Comp. y Sist. vol.19 no.2 México abr./jun. 2015 



A Photometric Sampling Strategy for Reflectance Characterization and Transference


Mario Castelán, Elier Cruz-Pérez, Luz Abril Torres-Méndez


Centro de Investigación y Estudios Avanzados del Instituto Politécnico Nacional, Grupo de Robótica y Manufactura Avanzada, Ramos Arizpe, Coah., México.

Corresponding author is Mario Castelán.


Article received on 20/02/2014.
Accepted on 06/05/2015.



Rendering 3D models with real world reflectance properties is an open research problem with significant applications in the field of computer graphics and image understanding. In this paper, our interest is in the characterization and transference of appearance from a source object onto a target 3D shape. To this end, a three-step strategy is proposed. In the first step, reflectance is sampled by rotating a light source in concentric circles around the source object. Singular value decomposition is then used for describing, in a pixel-wise manner, appearance features such as color, texture, and specular regions. The second step introduces a Markov random field transference method based on surface normal correspondence between the source object and a synthetic sphere. The aim of this step is to generate a sphere whose appearance emulates that of the source material. In the third step, final transference of properties is performed from the surface normals of the generated sphere to the surface normals of the target 3D model. Experimental evaluation validates the suitability of the proposed strategy for transferring appearance from a variety of materials between diverse shapes.

Keywords: Reflectance transference, singular value decomposition, random Markov fields.





1. Basri, R. & Jacobs, D. (2003). Lambertian reflectance and linear subspaces. IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 25, No. 6, pp. 383-390.         [ Links ]

2. Blanz, V. & Vetter, T. (1999). A morphable model for the synthesis of 3d faces. Proc. SIGGRAPH, volume 1, pp. 187-194.         [ Links ]

3. Chen, H., Belhumeur, P., & Jacobs, D. (2000). In search of illumination invariants. Proc. IEEE InternationalConference in ComputerVision andPattern Recognition, pp. 1-8.         [ Links ]

4. Cook, L. R. & Torrance, K. E. (1982). A reflectance model for computer graphics. ACM Trans. Graph., Vol. 1, pp. 7-24.         [ Links ]

5. Cross, G. R. & Jain, A. K. (1983). Markov random field texture models. Pattern Analysis and Machine Intelligence, IEEE Transactions on, Vol. 5, pp. 25-39.         [ Links ]

6. Curet (1999). Columbia-utrech Reflectance and Texture Database.         [ Links ]

7. Dana, K. J. (2001). Brdf/btf measurement device. Proc. IEEE International Conference in Computer Vision, volume 2, pp. 460-466.         [ Links ]

8. Dana, K. J., Ginneken, B. V., Nayar, S. K., & Koenderink, J. J. (1999). Reflectance and texture of real-world surfaces. ACM Trans. Graph., Vol. 18, pp. 1-34.         [ Links ]

9. Epstein, R., Hallinan, P. W., & Yuille, A. L. (1995). 5±2 eigenimages suffice: an empirical investigation of low-dimensional lighting models. Proc. Workshop on Physics-based Modelling in Computer Vision, pp. 108-116.         [ Links ]

10. F. E. Nicodemus, I. W. G., J. C. Richmond & Limperis, T. (1977). Geometrical considerations and nomenclature for reflectance. NBS Monograph.         [ Links ]

11. Ghosh, A., Heidrich, W., Achutha, S., & O'Toole, M. (2010). A basis illumination approach to BRDF measurement. International Journal of Computer Vision, Vol. 90, pp. 183-197.         [ Links ]

12. He, X. D., Torrance, K. E., Sillion, F. X., & Greenberg, D. P. (1991). A comprehensive physical model for light reflection. Proc. SIGGRAPH Computer Graphics, volume 25, pp. 175-186.         [ Links ]

13. Hernández-Rodríguez, F. & Castelán, M. (2012). A method for improving consistency in photometric databases. Proc. British Machine Vision Conference, pp. 1-10.         [ Links ]

14. Hertzmann, A. & Seitz, S. M. (2005). Example-based photometric stereo: Shape reconstruction with general, varying brdfs. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 27, No. 8, pp. 1254-1264.         [ Links ]

15. Liu, R. & Han, J. (2010). Recovering surface normal of specular object by hough transform method. IET Computer Vision, Vol. 4, No. 2, pp. 129-137.         [ Links ]

16. Marschner, S. R., Westin, S. H., Lafortune, E. P. F., & Torrance, K. E. (2000). Image-based bidirectional reflectance distribution function measurement. Appl. Opt., Vol. 39, No. 16, pp. 2592-2600.         [ Links ]

17. Matusik, W., Pfister, H., Brand, M., & McMillan, L. (2000). A data-driven reflectance model. ACM Trans. Graph., Vol. 22, No. 3, pp. 759-769.         [ Links ]

18. Mertens, T., Kautz, J., Chen, J., Bekaert, P., & Durand, F. (2006). Texture transfer using geometry correlation. Proc. Eurographics Symposium on Rendering, pp. 273-284.         [ Links ]

19. Murase, H. & Nayar, S. (1993). Parametric eigenspace representation for visual learning and recognition. Proc. SPIE Geometric Methods in Computer Vision II, volume 2031, pp. 378-391.         [ Links ]

20. Phong, B. T. (1975). Illumination for computer generated pictures. Communications of ACM, Vol. 18, No. 6, pp. 311-317.         [ Links ]

21. Ramamoorthi, R. (2002). Analytic pca reconstruction for theoretical analysis of lighting variability in images of a lambertian object. IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 24, No. 10, pp. 1322-1333.         [ Links ]

22. Saito, H., Omata, K., & Ozawa, S. (2003). Recovery of shape and surface reflectance of specular object from relative rotation of light source. Image and Vision Computing, Vol. 21, pp. 777-787.         [ Links ]

23. Sato, I., Okabe, T., Yu, Q., & Sato, Y. (2007). Shape reconstruction based on similarity in radiance changes under varying illumination. IEEE International Conference in Computer Vision, pp. 1-8.         [ Links ]

24. Standford_Repository (2011). The Stanford 3D scanning repository.         [ Links ]

25. Ward., G. J. (1992). Measuring and modeling anisotropic reflection. Proc. SIGGRAPH Comput. Graph., volume 24, pp. 265-272.         [ Links ]

Creative Commons License Todo el contenido de esta revista, excepto dónde está identificado, está bajo una Licencia Creative Commons