Title: Bringing Attention to CAD: Boundary Representation Learning via Transformer Show Chinese title

Title: 将注意力带到CAD：通过Transformer的边界表示学习

Abstract: The recent rise of generative artificial intelligence (AI), powered by Transformer networks, has achieved remarkable success in natural language processing, computer vision, and graphics. However, the application of Transformers in computer-aided design (CAD), particularly for processing boundary representation (B-rep) models, remains largely unexplored. To bridge this gap, we propose a novel approach for adapting Transformers to B-rep learning, called the Boundary Representation Transformer (BRT). B-rep models pose unique challenges due to their irregular topology and continuous geometric definitions, which are fundamentally different from the structured and discrete data Transformers are designed for. To address this, BRT proposes a continuous geometric embedding method that encodes B-rep surfaces (trimmed and untrimmed) into Bezier triangles, preserving their shape and continuity without discretization. Additionally, BRT employs a topology-aware embedding method that organizes these geometric embeddings into a sequence of discrete tokens suitable for Transformers, capturing both geometric and topological characteristics within B-rep models. This enables the Transformer's attention mechanism to effectively learn shape patterns and contextual semantics of boundary elements in a B-rep model. Extensive experiments demonstrate that BRT achieves state-of-the-art performance in part classification and feature recognition tasks. Show Chinese abstract

Abstract: 生成式人工智能（AI）的最近兴起，由Transformer网络驱动，在自然语言处理、计算机视觉和图形学领域取得了显著的成功。然而，Transformer在计算机辅助设计（CAD）中的应用，特别是处理边界表示（B-rep）模型，仍大多未被探索。为了填补这一空白，我们提出了一种将Transformer适应于B-rep学习的新方法，称为边界表示Transformer（BRT）。B-rep模型由于其不规则的拓扑结构和连续的几何定义而具有独特的挑战性，这与Transformer所设计用于的结构化和离散数据根本不同。为了解决这个问题，BRT提出了一种连续几何嵌入方法，将B-rep表面（修剪和未修剪）编码为贝塞尔三角形，无需离散化即可保留其形状和连续性。此外，BRT采用了一种拓扑感知的嵌入方法，将这些几何嵌入组织成适合Transformer的离散标记序列，从而在B-rep模型中捕捉几何和拓扑特征。这使得Transformer的注意力机制能够有效地学习B-rep模型中边界元素的形状模式和上下文语义。大量实验表明，BRT在部件分类和特征识别任务中达到了最先进的性能。