Abstract
In this report, we present a 3D shape modeling method using the shape’s silhouettes from multiple views to determine the model (polyhedron) parameters. The polyhedron parameters are determined by neural networks, each of which represents the model’s silhouette observed from a view point, and determines the polyhedron parameters by the back propagation algorithm so that the model’s silhouette from each view approximates the corresponding silhouette of the target shape. By conducting basic experiments, we verified the effectiveness of the method.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bardinet, E., Cohen, L.D., 1998. A Parametric Deformable Model to Fit Unstructured 3D Data. Computer Vision and Image Understanding 71,(1), 39–54.
Barr, A., 1984. Global and Local Deformations of Solid Primitives, Computer Graphics, 18, 21–30.
Chan, M., Metaxas, D., 1994 Physics-Based Object Pose and Shape Estimation from Multiple Views, Proc. International Conference on Pattern Recognition, Vol. 1, IEEE Computer Society Press, 326–330.
Chung, P.C., Tsai, C.T, Y.N. Sun., 1994. Polygonal Approximation Using a Competitive Hopfield Neural Network. Pattern Recognition 27, 1, 1,505-1,512. Fig. 12.Exp.2 Shape modeling experiment for an airplane shape. A polyhedron with 114 vertices was used.
Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J., 1992 Training Models of Shape from Sets of Examples, Proc. British Machine Vision Conference, Springer-Verlag, 9–18.
Hartley, R.I., 1997. In defense of the eight-point algorithm, IEEE Trans. Pattern Analysis and Machine Intelligence 19,(6), 580–593.
Igarashi, T., Matsuoka, S., Tanaka, H., 1999. Teddy:A Sketching Interface for 3D Freeform Design, ACM SIGGRAPH’99, Los Angels, 409–416.
Jain, A.K., Zhong, Y., Lakshmanan, S., 1996. Object Matching Using Deformable Templates, IEEE Trans. Pattern Analysis and Machine Intelligence 18, (3), 267–278.
Kumazawa, I., 2000. Compact and parametric shape representation by a tree of sigmoid functions for automatic shape modeling, Pattern Recognition Letters 21 651–660.
Shum, H.Y., Hebert, M., Ikeuchi, K., Reddy, R., 1997. An Integral Approach to Free-Form Object Modeling, IEEE Trans. Pattern Analysis and Machine Intelligence 19, (12), 1,366–1,375.
Zhang, Z., 1998. Image-based Geometrically-Correct Photorealistic Scene/Object Modeling(IBPhM):A Review, Proc. 3rd Asian Conference on Computer Vision (ACCV’98), 340–349.
Zhang, Z., 1998 Determining the epipolar geometry and its uncertainty:A review, International Jourmal of Computer Vision.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2001 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kumazawa, I., Ohno, M. (2001). 3D Shape Reconstruction from Multiple Silhouettes: Generalization from Few Views by Neural Network Learning. In: Arcelli, C., Cordella, L.P., di Baja, G.S. (eds) Visual Form 2001. IWVF 2001. Lecture Notes in Computer Science, vol 2059. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45129-3_63
Download citation
DOI: https://doi.org/10.1007/3-540-45129-3_63
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-42120-7
Online ISBN: 978-3-540-45129-7
eBook Packages: Springer Book Archive