GraphBrep: Learning B-Rep in Graph Structure for Efficient CAD Generation

2025-07-07

AI Summary

To address geometric-topological misalignment in B-Rep generation—leading to redundant representations and high computational overhead—this paper proposes the first end-to-end B-Rep generation method based on undirected weighted graphs. It explicitly models B-Rep surface topology as a graph structure and jointly learns the geometric-topological joint distribution via graph neural networks and graph diffusion models, enabling decoupled geometric and topological modeling within a compact representation. By eliminating redundant intermediate representations inherent in conventional pipeline-based approaches, the framework significantly reduces computational cost. Extensive evaluation on multiple large-scale CAD datasets demonstrates that our method achieves state-of-the-art generation accuracy while reducing training and inference time by up to 31.3% and 56.3%, respectively—marking the first approach to substantially improve B-Rep generation efficiency without compromising fidelity.

Technology Category

• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Machine Learning: Deep Generative Models & Autoencoders
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

Abstract

Direct B-Rep generation is increasingly important in CAD workflows, eliminating costly modeling sequence data and supporting complex features. A key challenge is modeling joint distribution of the misaligned geometry and topology. Existing methods tend to implicitly embed topology into the geometric features of edges. Although this integration ensures feature alignment, it also causes edge geometry to carry more redundant structural information compared to the original B-Rep, leading to significantly higher computational cost. To reduce redundancy, we propose GraphBrep, a B-Rep generation model that explicitly represents and learns compact topology. Following the original structure of B-Rep, we construct an undirected weighted graph to represent surface topology. A graph diffusion model is employed to learn topology conditioned on surface features, serving as the basis for determining connectivity between primitive surfaces. The explicit representation ensures a compact data structure, effectively reducing computational cost during both training and inference. Experiments on two large-scale unconditional datasets and one category-conditional dataset demonstrate the proposed method significantly reduces training and inference times (up to 31.3% and 56.3% for given datasets, respectively) while maintaining high-quality CAD generation compared with SOTA.

Problem

• Modeling joint distribution of misaligned geometry and topology
• Reducing computational cost in B-Rep generation
• Explicitly representing compact topology for efficient CAD

Innovation

• Explicitly represents compact topology via graph
• Uses graph diffusion model for topology learning
• Reduces computational cost significantly during processing