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3D High Resolution Mesh Deformation Based on Multi Library Wavelet Neural Network Architecture

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3D Research

Abstract

This paper deals with the features of a novel technique for large Laplacian boundary deformations using estimated rotations. The proposed method is based on a Multi Library Wavelet Neural Network structure founded on several mother wavelet families (MLWNN). The objective is to align features of mesh and minimize distortion with a fixed feature that minimizes the sum of the distances between all corresponding vertices. New mesh deformation method worked in the domain of Region of Interest (ROI). Our approach computes deformed ROI, updates and optimizes it to align features of mesh based on MLWNN and spherical parameterization configuration. This structure has the advantage of constructing the network by several mother wavelets to solve high dimensions problem using the best wavelet mother that models the signal better. The simulation test achieved the robustness and speed considerations when developing deformation methodologies. The Mean-Square Error and the ratio of deformation are low compared to other works from the state of the art. Our approach minimizes distortions with fixed features to have a well reconstructed object.

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Authors and Affiliations

  1. REGIM: Research Groups on Intelligent Machines, National School of Engineers (ENIS), University of Sfax, 3038, Sfax, Tunisia

    Naziha Dhibi, Akram Elkefi, Wajdi Bellil & Chokri Ben Amar

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  1. Naziha Dhibi
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  2. Akram Elkefi
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  3. Wajdi Bellil
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  4. Chokri Ben Amar
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Correspondence to Naziha Dhibi.

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Dhibi, N., Elkefi, A., Bellil, W. et al. 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

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  • DOI: https://doi.org/10.1007/s13319-016-0107-6

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