Polyhedral meshes are increasingly becoming an attractive option with particular advantages over traditional meshes for certain applications. What has been missing is a robust polyhedral meshing algorithm that can handle broad classes of domains exhibiting arbitrarily curved boundaries and sharp features. In addition, the power of primal-dual mesh pairs, exemplified by Voronoi-Delaunay meshes, has been recognized as an important ingredient in numerous formulations. The VoroCrust algorithm is the first provably correct algorithm for conforming polyhedral Voronoi meshing for non-convex and non-manifold domains with guarantees on the quality of both surface and volume elements. A robust refinement process estimates a suitable sizing field that enables the careful placement of Voronoi seeds across the surface, circumventing the need for clipping and avoiding its many drawbacks. The algorithm has the flexibility of filling the interior by either structured or random samples while preserving all sharp features in the output mesh. We demonstrate the capabilities of the algorithm on a variety of models and compare against state-of-the-art polyhedral meshing methods based on clipped Voronoi cells establishing the clear advantage of VoroCrust output.

中文翻译：

---

VoroCrust

多面体网格正日益成为一种有吸引力的选择，在某些应用中具有优于传统网格的特殊优势。缺少的是一种强大的多面体网格划分算法，该算法可以处理显示任意弯曲边界和尖锐特征的广泛类别的域。此外，原始双网格对（以 Voronoi-Delaunay 网格为例）的强大功能已被认为是众多配方中的重要成分。VoroCrust 算法是第一个可证明正确的算法，用于对非凸和非流形域进行多面体 Voronoi 网格划分，并保证表面和体积元素的质量。一个强大的细化过程估计一个合适的大小场，使 Voronoi 种子能够小心地放置在表面上，规避了裁剪的需要并避免了它的许多缺点。该算法具有通过结构化或随机样本填充内部的灵活性，同时保留输出网格中的所有尖锐特征。我们展示了该算法在各种模型上的能力，并与基于裁剪的 Voronoi 单元的最先进的多面体网格划分方法进行了比较，从而确定了 VoroCrust 输出的明显优势。