TG-Diff: Coupling Discrete Topology Diffusion and Topology-conditioned Geometry Diffusions for B-Rep Generation

Abstract

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.