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arXiv 0803.2331math.NAcs.NAmath.DG
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曲面网格一阶和二阶微分量的一致计算

Consistent Computation of First- and Second-Order Differential Quantities for Surface Meshes

Stony Brook University(石溪大学);Georgia Institute of Technology(佐治亚理工学院)

Xiangmin Jiao, Hongyuan Zha

AI总结:

本文通过高度函数的梯度和Hessian矩阵,推导出光滑曲面的一阶和二阶微分量(法向量和主曲率张量)与高度函数导数之间的转换公式,并提出基于加权最小二乘局部多项式拟合的数值框架,通过迭代拟合提高精度,实现曲面网格上微分量的一致计算。

摘要:

微分量,包括法线、曲率、主方向及相关矩阵,在几何处理和基于物理的建模中起着基础作用。在曲面网格上一致地计算这些微分量既重要又具有挑战性,一些现有方法常常产生不一致的结果,需要临时修复。在本文中,我们表明高度函数的梯度和Hessian矩阵的计算为一致地计算微分量提供了基础。我们推导了光滑曲面的一阶和二阶微分量(即法向量和主曲率张量)与其对应高度函数的一阶和二阶导数(即梯度和Hessian矩阵)之间转换的简单显式公式。然后,我们研究了一个通用、灵活的数值框架,基于局部多项式拟合(表述为加权最小二乘近似)来估计高度函数的导数。我们还提出了一种迭代拟合方案以提高精度。该框架推广了多项式拟合,并解决了其一些精度和稳定性问题,正如我们的理论分析和实验结果所证明的那样。

英文摘要:

Differential quantities, including normals, curvatures, principal directions, and associated matrices, play a fundamental role in geometric processing and physics-based modeling. Computing these differential quantities consistently on surface meshes is important and challenging, and some existing methods often produce inconsistent results and require ad hoc fixes. In this paper, we show that the computation of the gradient and Hessian of a height function provides the foundation for consistently computing the differential quantities. We derive simple, explicit formulas for the transformations between the first- and second-order differential quantities (i.e., normal vector and principal curvature tensor) of a smooth surface and the first- and second-order derivatives (i.e., gradient and Hessian) of its corresponding height function. We then investigate a general, flexible numerical framework to estimate the derivatives of the height function based on local polynomial fittings formulated as weighted least squares approximations. We also propose an iterative fitting scheme to improve accuracy. This framework generalizes polynomial fitting and addresses some of its accuracy and stability issues, as demonstrated by our theoretical analysis as well as experimental results.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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