Hexahedral meshes are widely used in simulation pipelines, yet automatic generation remains challenging for complex CAD geometries. Polycube-based hexahedral meshing is a representative approach due to its regular, parameterization-friendly structure, but existing polycube construction methods often rely on intricate surface segmentation and local heuristics, which can produce artifacts or fail on difficult shapes. In this paper, we propose an end-to-end framework for polycube generation based on conditional diffusion models. Given an input geometry represented as a point cloud, our method directly produces a corresponding polycube point cloud, eliminating the need for explicit surface segmentation or predefined polycube templates. At the core of our approach is a dual-latent conditional diffusion architecture that confines computationally expensive self-attention operations to a fixed-capacity, low-dimensional latent space. This design effectively decouples computational complexity from the resolution of both the input geometry and the output polycube, thereby avoiding the quadratic cost typical of point cloud self-attention mechanisms while supporting flexible input and output resolutions. To obtain a hexahedral mesh, the generated polycube is aligned to the input shape via rigid and non-rigid point cloud registration to establish surface correspondence, followed by a polycube-to-hex pipeline. We additionally create and release a paired dataset of CAD meshes and their corresponding polycube meshes, together with the core implementation of our model. Experiments show that PolycubeNet generalizes to complex CAD models with arbitrary genus and produces high-quality polycube structures within seconds, improving robustness and efficiency over prior learning-based approaches.

翻译：六面体网格在仿真流程中应用广泛，但其自动生成对复杂CAD几何体仍具挑战性。基于多立方体的六面体网格划分因其规则且易于参数化的结构成为代表性方法，然而现有构造技术常依赖复杂的表面分割和局部启发式算法，易产生伪影或在复杂形体上失效。本文提出基于条件扩散模型的端到端多立方体生成框架。该方法以点云形式输入几何体，直接生成对应的多立方体点云，无需显式表面分割或预定义模板。核心创新在于双潜条件扩散架构：将计算密集型自注意力操作约束至固定容量低维潜空间，有效解耦计算复杂度与输入几何体及输出多立方体的分辨率，从而规避点云自注意力机制典型的二次方开销，同时支持灵活的分辨率设置。为获取六面体网格，通过刚性与非刚性点云配准将生成的多立方体对齐至输入形状建立表面对应关系，再经多立方体-六面体转换流程完成网格生成。我们另构建并公开发布了包含CAD网格及其对应多立方体网格的配对数据集，同时提供模型核心实现。实验表明，PolycubeNet可泛化至任意亏格的复杂CAD模型，数秒内生成高质量多立方体结构，在鲁棒性和效率上均优于现有学习方法。