Boundary Representation (B-Rep) is the widely adopted standard in Computer-Aided Design (CAD) and manufacturing. However, generative modeling of B-Reps remains a formidable challenge due to their inherent heterogeneity as geometric cell complexes, which entangles topology with geometry across cells of varying orders (i.e., $k$-cells such as vertices, edges, faces). Previous methods typically rely on cascaded sequences to handle this hierarchy, which fails to fully exploit the geometric relationships between cells, such as adjacency and sharing, limiting context awareness and error recovery. To fill this gap, we introduce a novel paradigm that reformulates B-Reps into sets of compositional $k$-cell particles. Our approach encodes each topological entity as a composition of particles, where adjacent cells share identical latents at their interfaces, thereby promoting geometric coupling along shared boundaries. By decoupling the rigid hierarchy, our representation unifies vertices, edges, and faces, enabling the joint generation of topology and geometry with global context awareness. We synthesize these particle sets using a multi-modal flow matching framework to handle unconditional generation as well as precise conditional tasks, such as 3D reconstruction from single-view or point cloud. Furthermore, the explicit and localized nature of our representation naturally extends to downstream tasks like local in-painting and enables the direct synthesis of non-manifold structures (e.g., wireframes). Extensive experiments demonstrate that our method produces high-fidelity CAD models with superior validity and editability compared to state-of-the-art methods.

翻译：边界表示（B-Rep）是计算机辅助设计（CAD）与制造领域广泛采用的标准。然而，由于B-Rep作为几何单元复形固有的异质性——其将拓扑结构与不同阶次单元（即$k$单元，如顶点、边、面）的几何属性相互纠缠，生成式建模仍面临巨大挑战。现有方法通常依赖级联序列处理这种层次结构，未能充分利用单元间的几何关系（如邻接与共享），限制了上下文感知与错误恢复能力。为填补这一空白，我们提出一种新范式，将B-Rep重构为组合$k$单元粒子集合。该方法将每个拓扑实体编码为粒子组合，其中相邻单元在接口处共享相同隐变量，从而促进沿共享边界的几何耦合。通过解耦刚性层次结构，我们的表征统一了顶点、边和面，实现了具备全局上下文感知的拓扑与几何联合生成。我们采用多模态流匹配框架合成这些粒子集合，以处理无条件生成及精确条件任务（如单视图或点云的三维重建）。此外，该表征的显式与局部特性自然延伸至下游任务（如局部修复），并支持直接合成非流形结构（如线框）。大量实验表明，相较于现有先进方法，本方法能生成具有更高保真度、优越有效性与可编辑性的CAD模型。