Boundary representation (B-rep) is the industry standard for computer-aided design (CAD). While deep learning shows promise in processing B-rep models, existing methods suffer from a representation gap: continuous approaches offer analytical precision but are visually abstract, whereas discrete methods provide intuitive clarity at the expense of geometric precision. To bridge this gap, we introduce Brep2Shape, a novel self-supervised pre-training method designed to align abstract boundary representations with intuitive shape representations. Our method employs a geometry-aware task where the model learns to predict dense spatial points from parametric Bézier control points, enabling the network to better understand physical manifolds derived from abstract coefficients. To enhance this alignment, we propose a Dual Transformer backbone with parallel streams that independently encode surface and curve tokens to capture their distinct geometric properties. Moreover, the topology attention is integrated to model the interdependencies between surfaces and curves, thereby maintaining topological consistency. Experimental results demonstrate that Brep2Shape offers significant scalability, achieving state-of-the-art accuracy and faster convergence across various downstream tasks.Code is available at this repository: https://github.com/thuml/Brep2Shape.

翻译：边界表示（B-rep）是计算机辅助设计（CAD）的行业标准。尽管深度学习在B-rep模型处理方面展现出潜力，但现有方法面临表示鸿沟：连续方法具有解析精度但视觉表达抽象，而离散方法虽提供直观清晰性却牺牲了几何精度。为弥合这一差距，我们提出Brep2Shape，一种新颖的自监督预训练方法，旨在将抽象边界表示与直观形状表示进行对齐。该方法采用几何感知任务，通过让模型从参数化贝塞尔控制点预测密集空间点，使网络能够更好地理解源自抽象系数的物理流形。为增强这种对齐，我们提出双Transformer骨干网络，利用并行流分别独立编码曲面和曲线标记，以捕获其不同的几何属性。此外，还集成了拓扑注意力机制来建模曲面与曲线之间的相互依赖关系，从而保持拓扑一致性。实验结果表明，Brep2Shape具有良好的可扩展性，在多种下游任务中实现了最先进的准确率和更快的收敛速度。代码开源仓库地址：https://github.com/thuml/Brep2Shape。