In this paper, we propose an improved singularity structure simplification method for hexahedral (hex) meshes using a weighted ranking approach. In previous work, the selection of to-be-collapsed base complex sheets/chords is only based on their thickness, which will introduce a few closed-loops and cause an early termination of simplification and a slow convergence rate. In this paper, a new weighted ranking function is proposed by combining the valence prediction function of local singularity structure, shape quality metric of elements and the width of base complex sheets/chords together. Adaptive refinement and local optimization are also introduced to improve the uniformity and aspect ratio of mesh elements. Compared to thickness ranking methods, our weighted ranking approach can yield a simpler singularity structure with fewer base-complex components, while achieving comparable Hausdorff distance ratio and better mesh quality. Comparisons on a hex-mesh dataset are performed to demonstrate the effectiveness of the proposed method.

在本文中，我们提出了一种使用加权排序方法对六面体（hex）网格进行改进的奇异结构简化方法。在以前的工作中，要折叠的基础复杂薄板/和弦的选择仅基于其厚度，这将引入一些闭环并导致简化的早期终止和缓慢的收敛速度。本文通过结合局部奇异结构的化合价预测函数，元素的形状质量度量和基础复数个和弦的宽度，提出了一种新的加权排序函数。还引入了自适应细化和局部优化，以提高网格元素的均匀性和纵横比。与厚度排序方法相比，我们的加权排序方法可以产生更简单的奇异结构，具有更少的基础复杂组件，同时获得可比的Hausdorff距离比和更好的网格质量。在十六进制网格数据集上进行比较以证明所提出方法的有效性。