Abstract
Shape-from-Shading (SfS) is a fundamental problem in Computer Vision. A very common assumption in this field is that image projection is orthographic. This paper re-examines the basis of SfS, the image irradiance equation, under a perspective projection assumption. The resultant equation does not depend on the depth function directly, but rather, on its natural logarithm. As such, it is invariant to scale changes of the depth function. A reconstruction method based on the perspective formula is then suggested; it is a modification of the Fast Marching method of Kimmel and Sethian. Following that, a comparison of the orthographic Fast Marching, perspective Fast Marching and the perspective algorithm of Prados and Faugeras on synthetic images is presented. The two perspective methods show better reconstruction results than the orthographic. The algorithm of Prados and Faugeras equates with the perspective Fast Marching. Following that, a comparison of the orthographic and perspective versions of the Fast Marching method on endoscopic images is introduced. The perspective algorithm outperformed the orthographic one. These findings suggest that the more realistic set of assumptions of perspective SfS improves reconstruction significantly with respect to orthographic SfS. The findings also provide evidence that perspective SfS can be used for real-life applications in fields such as endoscopy.
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Bichsel, M. and Pentland, A.P. 1992. A simple algorithm for Shape from Shading. In Computer Vision and Pattern Recognition, pp. 459–465.
Brook, M.J. and Chojnacki, W. 1994. Direct computation of Shape from Shading. In Proceedings of the International Conference on Pattern Recognition, Israel, pp. 114–119.
Horn, B. 1975. Obtaining Shape from Shading information. In The Psychology of Computer Vision, P.H. Winston (ed.), Computer Science Series. McGraw-Hill Book Company, Chapt. 4, pp. 115–155.
Horn, B.K.P. 1977. Image intensity understanding. Artificial Intelligence, 8(2):201–231.
Horn, B.K.P. 1986. Robot Vision. The MIT Press/McGraw-Hill Book Company.
Horn, B.K.P. and Brooks, M.J. (eds.) 1989, Shape from Shading. The MIT Press.
Ishii, H. 1987. A simple, direct proof of uniqueness for solutions of the hamilton-jacobi equations of eikonal type. Proceedings of the American Mathematical Society, 100(5):247–251.
Kimmel, R. and Bruckstein, A.M. 1995. Global Shape from Shading. Computer Vision and Image Understanding, 62(3):360–369.
Kimmel, R. and Sethian, J.A. 2001. Optimal algorithm for Shape from Shading and path planning. Journal of Mathematical Imaging and Vision, 14(3):237–244.
Lee, C.-H. and Rosenfeld, A. 1985. Improved methods of estimating Shape from Shading using the light source coordinate system. Artificial Intelligence, 26:125–143.
Lee, K.M. and Kuo, C.-C.J. 1993. Shape from Shading with a linear triangular element surface model. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(8):815–822.
Lee, K.M. and Kuo, C.-C.J. 1997. Shape from Shading with a generalized reflectance map model. Computer Vision and Image Understanding, 67(2):143–160.
Levin, D. 2004. Mesh-independent surface interpolation. In Geometric Modeling for Scientific Visualization, G. Brunnett, B. Hamann, H. Mueller, and L. Linsen (eds.), Mathematics and Visualization, Springer-Verlag.
Lions, P.-L. 1982. Generalized Solutions of Hamilton-Jacobi Equations. Pitman: London.
Osher, S. and Sethian, J.A. 1988. Fronts propagating with curvature dependent speed: Algorithms based on hamilton-jacobi formulation. Journal of Computational Physics, 79:12–49.
Penna, M.A. 1989. ‘A Shape from Shading analysis for a single perspective image of a polyhedron. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(6):545–554.
Pentland, A.P. 1984. Local shading analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6(2):170–187.
Prados, E. and Faugeras, O. 2003. Perspective Shape from Shading and viscosity solutions. In Proceedings of the 9th IEEE International Conference on Computer Vision, vol. 2, pp. 826–831.
Prados, E., Faugeras, O., and Rouy, E. 2002. Shape from Shading and viscosity solutions. In 7th European Conference on Computer Vision, A. Heyden, G. Sparr, M. Nielsen, and P. Johansen (eds.), vol. II. Copenhagen, Denmark, pp. 790–804.
Robles-Kelly, A. and Hancock, E.R. 2002. Model acquisition using Shape-from-Shading. In The 2nd International Workshop on Articulated Motion and Deformable Objects. F.J. Perales and E.R. Hancock (eds.), Palma de Mallorca, Springer, pp. 43–55.
Rouy, E. and Tourin, A. 1992. A viscosity solutions approach to Shape-from-Shading. SIAM Journal of Numerical Analysis, 29(3):867–884.
Samaras, D. and Metaxas, D. 2003. Illumination constraints in deformable models for shape and light direction estimation. IEEE Transactions on Pattern Analisys and Machine Intelligence.
Seong, I., Hideo, S., and Shinji, O. 1997. A divide-and-conquer strategy in Shape from Shading problem. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition. pp. 413–419.
Sethian, J.A. 1996. A fast marching level set method for monotonically advancing fronts. Proceedings of the National Academy of Science of the USA, 93:1591–1595.
Sethian, J.A. 1999. Level Set Methods and Fast Marching Methods: Evolving Interfaces in Computational Geometry, Fluid Mechanics, Computer Vision, and Materials Science, 2nd ed. Cambridge Monograph on Applied and Computational Mathematics. Cambridge University Press.
Tankus, A., Sochen, N., and Yeshurun, Y. 2003. A new perspective on Shape-from-Shading. In Proceedings of the 9th IEEE International Conference on Computer Vision, vol. II. Nice, France, pp. 862–869.
Tankus, A. 2004. Perspective Shape-from-Shading. Ph.D. thesis, Tel-Aviv University.
Tsai, P.-S. and Shah, M. 1994. Shape from Shading using linear approximation. Image and Vision Computing, 12(8):487–498.
Weiss, I. 1997. A perspective 3D formalism for Shape from Shading. In Proceedings of DARPA Image Understanding Workshop, Vol. 2. pp. 1393–1402.
Yamany, S.M., Farag, A.A., Rickard, E., Tasman, D., and Farman, A.G. 1999. A robust 3D reconstruction system for human jaw modeling. In Proceedings of the Second International Conference Medical Image Computing and Computer-Assisted Intervention (MICCAI). Berlin, Germany, Springer-Verlag, pp. 778–787.
Yuen, S.Y., Tsui, Y.Y., Leung, Y.W., and Chen, R.M.M. 2002. Fast marching method for shape from shading under perspective projection. In Proceedings of Second IASTED International Conference Visualization, Imaging, and Image Processing,V.J.J. (ed.),Malaga, Spain, ACTA Press, Anaheim, CA, USA, pp. 584–589.
Zhang, R., Tsai, P.-S., Cryer, J.E., and Shah, M. 1999. Shape from Shading: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 21(8):690–705.
Zhao, W. and Chellappa, R. 2000. Face recognition using symmetric Shape from Shading. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 4. Hilton Head, SC, pp. 286–293.
Zheng, Q. and Chellappa, R. 1991. Estimation of illuminant direction, albedo, and Shape from Shading. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13(7):680–702.
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This research has been supported in part by Tel-Aviv University fund, the Adams Super-Center for Brain Studies, the Israeli Ministry of Science, the ISF Center for Excellence in Applied Geometry, the Minerva Center for geometry, and the A.M.N. fund.
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Tankus, A., Sochen, N. & Yeshurun, Y. Shape-from-Shading Under Perspective Projection. Int J Comput Vision 63, 21–43 (2005). https://doi.org/10.1007/s11263-005-4945-6
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DOI: https://doi.org/10.1007/s11263-005-4945-6