We present a new software package, "HybridOctree_Hex," for adaptive all-hexahedral mesh generation based on hybrid octree and quality improvement with Jacobian control. The proposed HybridOctree_Hex begins by detecting curvatures and narrow regions of the input boundary to identify key surface features and initialize an octree structure. Subsequently, a strongly balanced octree is constructed using the balancing and pairing rules. Inspired by our earlier preliminary hybrid octree-based work, templates are designed to guarantee an all-hexahedral dual mesh generation directly from the strongly balanced octree. With these pre-defined templates, the sophisticated hybrid octree construction step is skipped to achieve an efficient implementation. After that, elements outside and around the boundary are removed to create a core mesh. The boundary points of the core mesh are connected to their corresponding closest points on the surface to fill the buffer zone and build the final mesh. Coupled with smart Laplacian smoothing, HybridOctree_Hex takes advantage of a delicate optimization-based quality improvement method considering geometric fitting, Jacobian and scaled Jacobian, to achieve a minimum scaled Jacobian that is higher than $0.5$. We empirically verify the robustness and efficiency of our method by running the HybridOctree_Hex software on dozens of complex 3D models without any manual intervention or parameter adjustment. We provide the HybridOctree_Hex source code, along with comprehensive results encompassing the input and output files and statistical data in the following repository: https://github.com/CMU-CBML/HybridOctree_Hex.

翻译：我们提出一种新型软件包"HybridOctree_Hex"，用于基于混合八叉树结构并配合雅可比控制质量优化的适应性全六面体网格生成。该方法首先检测输入边界的曲率与狭窄区域，以识别关键表面特征并初始化八叉树结构；随后通过平衡与配对规则构建强平衡八叉树。受前期混合八叉树研究的启发，我们设计了一组模板，可基于强平衡八叉树直接生成全六面体对偶网格。借助预设模板，该方法跳过了复杂的混合八叉树构造步骤，从而实现了高效实现。此后，移除边界外部及周边的单元以生成核心网格，并将核心网格的边界点连接至曲面上最近对应点，以填充缓冲区域并生成最终网格。结合智能拉普拉斯平滑，HybridOctree_Hex采用一种兼顾几何拟合、雅可比矩阵与缩放雅可比矩阵的精细优化质量改善方法，使最小缩放雅可比矩阵值高于$0.5$。通过在数十个复杂三维模型上运行HybridOctree_Hex软件包且无需任何人工干预或参数调整，我们实证验证了该方法的鲁棒性与效率。HybridOctree_Hex源代码及涵盖输入输出文件与统计数据的完整结果均可通过以下仓库获取：https://github.com/CMU-CBML/HybridOctree_Hex。