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用于Loop和Catmull-Clark细分曲面插值的共轭梯度渐进迭代逼近

Conjugate-Gradient Progressive-Iterative Approximation for Loop and Catmull-Clark Subdivision Surface Interpolation

  • 摘要: 1、研究背景(context):渐进迭代逼近是一种有效的数据插值方法,在细分曲面拟合、参数曲线曲面拟合等领域有广泛应用。经典的渐进迭代逼近是一种梯度下降法,收敛速度较慢。加权渐进迭代逼近虽然可以在一定程度上加速收敛,但它仍然是一种梯度下降算法。因此,如何发展新方法对渐进迭代逼近进行较大程度的加速,成为渐进迭代研究中一个迫切需要解决的问题。
    2、目的(Objective):本文的研究目的是设计一种比经典渐进迭代逼近和加权渐进迭代逼近收敛更快的算法。由于经典渐进迭代逼近和加权渐进迭代逼近都属于梯度下降法,而共轭梯度法比梯度下降法收敛更快,因此,本文基于共轭梯度法设计一种新型渐进迭代逼近算法(CG-PIA),收敛速度快于经典渐进迭代逼近和加权渐进迭代逼近。
    3、方法(Method):本文提出的CG-PIA是一种迭代方法,在每一步迭代中,交替更新控制网格和梯度网格的网格顶点,而不改变控制网格拓扑连接关系。利用矩阵分析工具,可以证明CG-PIA迭代的收敛性。在本文中,CG-PIA方法在Loop和Catmull-Clark细分曲面拟合中,展示它的有效性和效率。
    4、结果(Result & Findings):理论分析证明,当系数矩阵是正定矩阵时,CG-PIA迭代法收敛。数值实验表明,虽然CG-PIA每次迭代计算时间要多于经典渐进迭代和加权渐进迭代,但是,达到相同的拟合精度,CG-PIA的迭代步数要远少于经典渐进迭代和加权渐进迭代。
    5、结论(Conclusions):CG-PIA可以应用于使得系数矩阵正定的细分曲面拟合问题,包括Loop细分和Catmull-Clark细分。CG-PIA生成的细分曲面,不但保持了原始网格的形状,还不改变原网格的连接关系。与经典渐进迭代和加权渐进迭代相比,CG-PIA的收敛速度要快的多。

     

    Abstract: Loop and Catmull-Clark are the most famous approximation subdivision schemes, but their limit surfaces do not interpolate the vertices of the given mesh. Progressive-iterative approximation (PIA) is an efficient method for data interpolation and has a wide range of applications in many fields such as subdivision surface fitting, parametric curve and surface fitting among others. However, the convergence rate of classical PIA is slow. In this paper, we present a new and fast PIA format for constructing interpolation subdivision surface that interpolates the vertices of a mesh with arbitrary topology. The proposed method, named Conjugate-Gradient Progressive-Iterative Approximation (CG-PIA), is based on the Conjugate-Gradient Iterative algorithm and the Progressive Iterative Approximation (PIA) algorithm. The method is presented using Loop and Catmull-Clark subdivision surfaces. CG-PIA preserves the features of the classical PIA method, such as the advantages of both the local and global scheme and resemblance with the given mesh. Moreover, CG-PIA has the following features. 1) It has a faster convergence rate compared with the classical PIA and W-PIA. 2) CG-PIA avoids the selection of weights compared with W-PIA. 3) CG-PIA does not need to modify the subdivision schemes compared with other methods with fairness measure. Numerous examples for Loop and Catmull-Clark subdivision surfaces are provided in this paper to demonstrate the efficiency and effectiveness of CG-PIA.

     

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