Mesh distortion optimization is a popular research topic and has wide range of applications in computer graphics, including geometry modeling, variational shape interpolation, UV parameterization, elastoplastic simulation, etc. In recent years, many solvers have been proposed to solve this nonlinear optimization efficiently, among which projected Newton has been shown to have best convergence rate and work well in both 2D and 3D applications. Traditional Newton approach suffers from ill conditioning and indefiniteness of local energy approximation. A crucial step in projected Newton is to fix this issue by projecting energy Hessian onto symmetric positive definite (SPD) cone so as to guarantee the search direction always pointing to decrease the energy locally. Such step relies on time consuming eigen decomposition of element Hessian, which has been addressed by several work before on how to obtain a conjugacy that is as diagonal as possible. In this report, we demonstrate an analytic form of Hessian eigen system for distortion energy defined using principal stretches, which is the most general representation. Compared with existing projected Newton diagonalization approaches, our formulation is more general as it doesn't require the energy to be representable by tensor invariants. In this report, we will only show the derivation for 3D and the extension to 2D case is straightforward.
翻译:网状扭曲优化是一个受欢迎的研究课题,在计算机图形中有着广泛的应用,包括几何模型、变形形状内推、紫外线参数化、弹性模拟等。近年来,提出了许多解决方案,以有效解决这种非线性优化,其中预测的牛顿在2D和3D应用中表现出最佳趋同率和效果良好。传统的牛顿方法受到当地能源近距离的不完善和不定期的制约。预测牛顿方法的一个重要步骤是,通过将赫森能源预测成正对正数(SPD)连接来解决这个问题,从而保证搜索方向总是指向减少当地能源。这一步骤依赖于时间消耗海森元素的eigen分解,而在此之前在如何获得尽可能对等的混凝度方面,牛顿方法已经得到了很好的处理。在这个报告中,我们展示了使用主伸缩定义的赫森艾根系统的一种分析形式,这是最一般的正数正数(SPD)连接,从而保证搜索方向总是指向减少当地能源的减少。这一步骤依靠时间消耗海珊分解的元素的分位法,在目前预测的公式中将要求我们更直截面的D的分解方式显示目前预测的分解方式。