AI 中文总结
研究提出TG-Diff框架，通过解耦拓扑与几何建模生成B-rep。基于以曲面为中心的表示开发两个扩散模型，拓扑扩散用离散扩散模型，曲面隐变量生成用条件隐变量扩散模型，经后处理形成封闭B-rep，计算量小且性能优越。
AI 中文摘要
边界表示（B-rep）是计算机辅助设计（CAD）的标准格式。本文提出了一个轻量级的基于扩散的两阶段B-rep生成框架TG-Diff，通过解耦拓扑和几何建模实现高效、高质量的B-rep生成。与之前将拓扑表示为顶点、边和曲面及其关系的集合不同，TG-Diff仅将拓扑表示为曲面及其邻接关系的集合。基于此，开发了两个独立的扩散模型分别生成曲面邻接关系和曲面隐变量。拓扑扩散模型采用离散扩散模型进行高效二元采样，曲面隐变量生成采用带轻量级DiT架构的条件隐变量扩散模型。最后通过后处理从解码的相邻曲面导出边和顶点形成最终的封闭B-rep。TG-Diff计算量小但在有效性指标上表现出色，在多个数据集的相关指标上性能优越。
英文摘要
Boundary representation (B-rep) is the standard format for computer-aided design (CAD). This article proposes a lightweight two-stage diffusion-based B-rep generation framework, TG-Diff, that achieves efficient, high-quality B-rep generation by decoupling topology and geometric modeling. In contrast to previous work that generates topology as a collection of vertices, edges, and surfaces together with their relationships, TG-Diff represents topology only as a collection of surfaces and their adjacency relationships. This surface-centric representation inherently alleviates the geometric and topological inconsistencies between separately generated surfaces, edges, and vertices, simplifying the generation process. Based on the surface-centric representation, we develop two independent diffusion models that generate surface adjacency relationships and surface latents, respectively. By using topology as guidance, the surface generation process becomes more stable, leading to stronger structural completeness in the generated B-rep models. The topology diffusion model adopts a Discrete Diffusion Model (D3PM) for efficient binary sampling, avoiding the slow inference of autoregressive methods. Surface latent generation employs a conditional latent diffusion model with a lightweight DiT architecture, where surface adjacency guides geometry generation while reducing computational cost. Finally, edges and vertices are derived from the decoded adjacent surfaces via post-processing to form a final watertight B-rep. Despite its compact computational footprint (82.18M parameters and 2.2 GFLOPs), TG-Diff excels in the validity metric while achieving superior performance on all COV, MMD, and JSD metrics across the DeepCAD and ABC datasets.
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