Abstract
The recovery of the threedimensional structure of faces with conventional stereo methods still proves difficult. In this paper we introduce a higher order constraint based on linear object classes, which supplies a standard stereo algorithm with prior knowledge of the general structure of faces. This constraint has been learned by exploiting the similarities between 200 faces in a database and is represented in a morphable face model.
This combined approach has been tested and compared against an already existing method for estimating depth information using only prior knowledge and against the standard stereo algorithm.
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References
V. Blanz and T. Vetter. A Morphable Model for the Synthesis of 3D Faces. Computer Graphics Proceedings SIGGRAPH’99, 1999. Accepted.
I. J. Cox, S. L. Hingorani, B. M. Maggs, and S. B. Rao. A Maximum Likelihood Stereo Algorithm. Computer Vision and Image Understanding, 63 (3): 542–567, 1996.
U. R. Dhond and J. K. Aggarwal. Structure from Stereo - A Review. In IEEE Transaction on Systems, Man, and Cybernetics, 19(6):1489–1510, 1989.
L. Falkenhagen. Hierarchical Block-Based Disparity Estimation Considering Neighbourhood Constraints, International Workshop on SNHC and 3D Imaging, 1997.
O. Faugeras. Three-Dimensional Computer Vision. A Geometric Viewpoint. The MIT Press, Cambridge, 1993.
O. Faugeras and R. Keriven. Complete Dense Stereovision using Level Set Methods. In H. Burkhardt, B. Neumann, eds., Computer Vision–ECCV’98, Vol I, 379–393, 1998.
P. Fua and C. Miccio. From Regular Images to Animated Heads: A Least Squares Approach. In H. Burkhardt, B. Neumann, eds., Computer Vision–ECCV’98, Vol.I 188–202, 1998.
S. B. Kang. A Structure from Motion Approach using Constrained Deformable Models and Appearance Prediction. Digital Equipment Corporation, Cambridge Research Lab, Technical Report Series, CRL 97 /6, 1997.
R. Koch. M. Pollefevs and L. van Gool. Multi Viewpoint Stereo from Uncalibrated Video Sequences. In H. Burkhardt, B. Neumann, eds., Computer Vision–ECCV’98. VolI 55–71, 1998.
D. Marr and T. Poggio. A cooperative stereo algorithm. Science, 194, 1976.
P. S. Penev, J. J. Atick. Local Feature Analysis: A general statistical theory for object representation. Network: Computation in Neural Systems, 7 (3): 477–500, 1996.
E. Trucco. A. Verri. Introductory Techniques for 3-D Computer Vision. Prentice Hall. Upper Saddle River, 1998.
T. Vetter and V.Blanz. Estimating coloured 3D face models from single images: An example based approach. H.BurkhardtB.Neumann, eds., Computer Vision–ECCV’98, Vol. II, 499–513, 1998.
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Wallraven, C., Blanz, V., Vetter, T. (1999). 3D-Reconstruction of Faces: Combining Stereo with Class-Based Knowledge. In: Förstner, W., Buhmann, J.M., Faber, A., Faber, P. (eds) Mustererkennung 1999. Informatik aktuell. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-60243-6_47
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DOI: https://doi.org/10.1007/978-3-642-60243-6_47
Publisher Name: Springer, Berlin, Heidelberg
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