nips nips2004 nips2004-179 nips2004-179-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Jan E. Solem, Fredrik Kahl
Abstract: We consider the problem of geometrical surface reconstruction from one or several images using learned shape models. While humans can effortlessly retrieve 3D shape information, this inverse problem has turned out to be difficult to perform automatically. We introduce a framework based on level set surface reconstruction and shape models for achieving this goal. Through this merging, we obtain an efficient and robust method for reconstructing surfaces of an object category of interest. The shape model includes surface cues such as point, curve and silhouette features. Based on ideas from Active Shape Models, we show how both the geometry and the appearance of these features can be modelled consistently in a multi-view context. The complete surface is obtained by evolving a level set driven by a PDE, which tries to fit the surface to the inferred 3D features. In addition, an a priori 3D surface model is used to regularize the solution, in particular, where surface features are sparse. Experiments are demonstrated on a database of real face images.
[1] J.A. Sethian. Level Set Methods and Fast Marching Methods Evolving Interfaces in Computational Geometry, Fluid Mechanics, Computer Vision, and Materials Science. Cambridge University Press, 1999.
[2] T. F. Cootes and Taylor C. J. Active shape model search using local grey-level models: A quantatitative evaluation. In British Machine Vision Conf., pages 639–648, 1993.
[3] T.F. Cootes, G.V. Wheeler, K.N. Walker, and C.J. Taylor. View-based active appearance models. Image and Vision Computing, 20(9-10):657–664, 2002.
[4] M. E. Tipping and C. M. Bishop. Probabilistic principal component analysis. Phil. Trans. Royal Soc. London B, 61(3):611–622, 1999.
[5] V. Blanz and T. Vetter. A morphable model for the synthesis of 3d faces. In SIGGRAPH, pages 187–194, 1999.
[6] S. Romdhani and T. Vetter. Efficient, robust and accurate fitting of a 3d morphable model. In Int. Conf. Computer Vision, pages 59–66, Nice, France, 2003.
[7] P. Fua. Regularized bundle-adjustment to model heads from image sequences without calibration data. Int. J. Comput. Vision, 38(2):153–171, 2000.
[8] B. Moghaddam, J. Lee, H. Pfister, and R. Machiraju. Model-based 3d face capture with shapefrom-silhouettes. In IEEE International Workshop on Analysis and Modeling of Faces and Gestures (AMFG), pages 20–27, 2003.
[9] H.K. Zhao, S. Osher, B. Merriman, and M. Kang. Implicit and non-parametric shape reconstruction from unorganized points using a variational level set method. In Computer Vision and Image Understanding, pages 295–319, 2000.
[10] J.E. Solem and F. Kahl. Surface reconstruction from the projection of points, curves and contours. In 2nd Int. Symposium on 3D Data Processing, Visualization and Transmission, Thessaloniki, Greece, 2004.
[11] O. Faugeras and R. Keriven. Variational principles, surface evolution, PDEs, level set methods, and the stereo problem. IEEE Transactions on Image Processing, 7(3):336–344, 1998.
[12] M. Rousson and N. Paragios. Shape priors for level set representations. In Proc. European Conf. on Computer Vision, volume 2351 of Lecture Notes in Computer Science. Springer, 2002.
[13] K. Skoglund. Three-dimensional face modelling and analysis. Master’s thesis, Informatics and Mathematical Modelling, Technical University of Denmark, DTU, 2003.
[14] J.E. Solem and A. Heyden. Reconstructing open surfaces from unorganized data points. In International Conference on Computer Vision and Pattern Recognition, Washington DC, 2004.