Abstract
The 3D deformation and simulation process frequently include much iteration of geometric design changes. We propose in this paper a study on the influence of wavelet number change in the wavelet neural network architecture for 3D mesh deformation method. Our approach is focused on creating the series of intermediate objects to have the target object, using trust region spherical parameterization algorithm as a common domain of the source and target objects that minimizing angle and area distortions which assurance bijective 3D spherical parameterization, and we used a multi-library wavelet neural network structure (MLWNN) as an approximation tools for feature alignment between the source and the target models to guarantee a successful deformation process. Experimental results show that the spherical parameterization algorithm preserves angle and area distortion, a MLWNN structure relying on various mother wavelets families (MLWNN) to align mesh features and minimize distortion with fixed features, and the increasing of wavelets number makes it possible to facilitate the features alignment which implies the reduction of the error between the objects thus reducing the rate of deformation to have good deformation scheme.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Kshirsagar, S., Garchery, S., Magnenat Thalmann, N.: Feature point based mesh deformation applied to MPEG-4 facial animation. In: Magnenat-Thalmann, N., Thalmann, D. (eds.) The International Federation for Information Processing, vol. 68. Springer, Boston (2001). https://doi.org/10.1007/978-0-306-47002-8_3
Dhibi, N., Elkefi, A., Bellil, W., Amar, C.B.: 3D High resolution mesh deformation based on multi library wavelet neural network architecture. 3D Res. 7, 31 (2016). https://doi.org/10.1007/s13319-016-0107-6
Othmani, M., Bellil, W., Amar, C.B., Alimi, M.A.: A novel approach for high dimension 3D object representation using multi-mother wavelet network. Int. J. Multimedia Tools Appl. MTAP 59(1), 7–24 (2012). https://doi.org/10.1007/s11042-010-0697-6
Dhibi, N., Bellil, W., Amar, C.B.: Study implementation of a new training algorithm for wavelet network based on genetic algorithm and multiresolution analysis for 3D objects modeling. In: IEEE Mediterranean Electrotechnical Conference (2012)
Dhibi, N., Elkefi, A., Bellil, W., Amar, C.B.: A trust region optimization method for fast 3D spherical configuration in morphing processes. In: Battiato, S., Blanc-Talon, J., Gallo, G., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2015. LNCS, vol. 9386, pp. 541–552. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-25903-1_47
Floater, M.S.: Parameterization and smooth approximation of surface triangulation. Comput. Aid. Geom. Des. 14(3), 231–250 (1997)
Gu, X., Yau, S.T.: Global conformal surface parameterization. In: Proceedings of Symposium of Geometry Processing (2003)
Zayer, R., Rossl, C., Seidel, H.P.: Curvilinear spherical parameterization. In: Proceedings of IEEE International Conference on Shape Modeling and Applications (2006)
Praun, E., Hoppe, H.: Spherical parametrization and remeshing. ACM Trans. Graph. 22(3), 340–349 (2003)
Wan, S., Ye, T., Li, M., Zhang, H., Li, X.: Efficient spherical parametrization using progressive optimization. In: Hu, S.-M., Martin, R.R. (eds.) CVM 2012. LNCS, vol. 7633, pp. 170–177. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-34263-9_22
Gao, Y., Hao, A., Zhao, Q.: Skin-detached surface for interactive large mesh editing. In: Pan, Z., Cheok, A.D., Müller, W., Chang, M., Zhang, M. (eds.) Transactions on Edutainment VII. LNCS, vol. 7145, pp. 99–109. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-29050-3_9
Blanco, F.R., Manuel, M.: Instant mesh deformation. In: I3D 2008 Proceedings of the Symposium on Interactive3D Graphics and Games, pp. 71–78 (2008)
Sumner, R.W., Popovicn, J.: Deformation transfer for triangle meshes. In: ACM Transactions on Graphics (TOG) Proceedings of ACM SIGGRAPH (2004)
Golub, G.H., Van Loan, C.F.: Matrix Computations, 2nd edn. John Hopkins University Press, Baltimore (1989)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Dhibi, N., Elkefai, A., Amar, C.B. (2018). A Study on the Influence of Wavelet Number Change in the Wavelet Neural Network Architecture for 3D Mesh Deformation Using Trust Region Spherical Parameterization. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds) Artificial Neural Networks and Machine Learning – ICANN 2018. ICANN 2018. Lecture Notes in Computer Science(), vol 11140. Springer, Cham. https://doi.org/10.1007/978-3-030-01421-6_52
Download citation
DOI: https://doi.org/10.1007/978-3-030-01421-6_52
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-01420-9
Online ISBN: 978-3-030-01421-6
eBook Packages: Computer ScienceComputer Science (R0)