Growing Grid-Evolutionary Algorithm for Surface Reconstruction

P. Pandunata, F. Forkan, S. Shamsuddin
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引用次数: 1

Abstract

This work primarily aims at introducing an algorithm for surface construction in conjunction with hybrid Growing Grid network and Evolutionary Algorithm, called Growing Grid-Evolutionary network. The process of surface construction primarily consists of two main steps namely: parameterization and surface fitting. The application of growing grid network is implemented at the parameterization phase; meanwhile the evolutionary algorithm has been used to optimally fit the surfaces through the Non Uniform Relational B-Spline (NURBS) method. Various graphical data are used in the experiment including the free-form objects, parabola, and mask. In order to validate the proposed algorithm, we conduct an error analysis for each step of parameterization and surface fitting by comparing the surface images generated with the original surfaces. Experimental results show that the proposed growing grid-evolutionary network has successfully generated surfaces that resemble the original surfaces and enhance its performance.
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生长网格-进化曲面重建算法
本文主要介绍了一种结合生长网格网络和进化算法的曲面构建算法,称为生长网格-进化网络。曲面构造过程主要包括参数化和曲面拟合两个主要步骤。在参数化阶段实现了网格网络增长的应用;同时,采用进化算法通过非均匀关系b样条(NURBS)方法对曲面进行最优拟合。实验中使用了各种图形数据,包括自由形状物体、抛物线和掩模。为了验证所提出的算法,我们通过将生成的曲面图像与原始曲面进行比较,对参数化和曲面拟合的每一步进行误差分析。实验结果表明,所提出的生长网格进化网络成功地生成了与原始表面相似的表面,并提高了网络的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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