各向同性三角形重新网格化方法综述

三维网格模型的重新网格化是计算机图形学中的重要内容,是许多几何应用的关键组成部分。近年来迅速发展的三维处理技术,如有限元模拟、计算机动画、三维打印等,对网格质量的要求不断提升,促进了三维网格模型重新网格化的持续发展,由此产生了许多新的重新网格化技术。首先介绍了三角形网格质量评估的标准,然后概述了各向同性重新网格化的最新进展,并详细研究和比较了各种重新网格化算法的优缺点,最后对未来的研究提出了新的问题与方向。     

Some empirical advances in matrix completion

Solving the matrix completion problem via rank minimization is NP hard. Recent studies have shown that this problem can be addressed as a convex nuclear-norm minimization problem, albeit at an increase in the required number of samples. This paper proposes a non-convex optimization problem (a variant of convex nuclear-norm minimization) for the solution of matrix completion. It also develops a fast numerical algorithm to solve the optimization. This empirical study shows that significant improvement can be achieved by the proposed method compared to the previous ones.The number of required samples is also dependent on the type of sampling scheme used. This work shows that blue-noise sampling schemes yield more accurate matrix completion results compared to the popular uniform random sampling.