This paper describes an algorithm to extract adaptive and quality 3D meshes directly from volumetric imaging data. The extracted tetrahedral and hexahedral meshes are extensively used in the Finite Element Method (FEM). A top-down octree subdivision coupled with the dual contouring method is used to rapidly extract adaptive 3D finite element meshes with correct topology from volumetric imaging data. The edge contraction and smoothing methods are used to improve the mesh quality. The main contribution is extending the dual contouring method to crack-free interval volume 3D meshing with feature sensitive adaptation. Compared to other tetrahedral extraction methods from imaging data, our method generates adaptive and quality 3D meshes without introducing any hanging nodes. The algorithm has been successfully applied to constructing the geometric model of a biomolecule in finite element calculations.

中文翻译：

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来自成像数据的 3D 有限元网格划分

本文描述了一种直接从体积成像数据中提取自适应和高质量 3D 网格的算法。提取的四面体和六面体网格广泛用于有限元方法 (FEM)。自上而下的八叉树细分与双重轮廓方法相结合，用于从体积成像数据中快速提取具有正确拓扑结构的自适应 3D 有限元网格。边缘收缩和平滑方法用于提高网格质量。主要贡献是将双重轮廓法扩展到具有特征敏感自适应的无裂纹间隔体积 3D 网格划分。与从成像数据中提取其他四面体的方法相比，我们的方法生成自适应和高质量的 3D 网格，而不会引入任何悬挂节点。