Previous representation and generation approaches for the B-rep relied on graph-based representations that disentangle geometric and topological features through decoupled computational pipelines, thereby precluding the application of sequence-based generative frameworks, such as transformer architectures that have demonstrated remarkable performance. In this paper, we propose BrepARG, the first attempt to encode B-rep's geometry and topology into a holistic token sequence representation, enabling sequence-based B-rep generation with an autoregressive architecture. Specifically, BrepARG encodes B-rep into 3 types of tokens: geometry and position tokens representing geometric features, and face index tokens representing topology. Then the holistic token sequence is constructed hierarchically, starting with constructing the geometry blocks (i.e., faces and edges) using the above tokens, followed by geometry block sequencing. Finally, we assemble the holistic sequence representation for the entire B-rep. We also construct a transformer-based autoregressive model that learns the distribution over holistic token sequences via next-token prediction, using a multi-layer decoder-only architecture with causal masking. Experiments demonstrate that BrepARG achieves state-of-the-art (SOTA) performance. BrepARG validates the feasibility of representing B-rep as holistic token sequences, opening new directions for B-rep generation.

翻译：先前针对B-rep的表示与生成方法主要依赖于基于图的表示形式，这类方法通过解耦的计算流程分离几何特征与拓扑特征，从而无法应用基于序列的生成框架（例如已展现卓越性能的Transformer架构）。本文提出BrepARG，首次尝试将B-rep的几何与拓扑结构编码为整体标记序列表示，实现了基于自回归架构的序列式B-rep生成。具体而言，BrepARG将B-rep编码为三类标记：表征几何特征的几何与位置标记，以及表征拓扑关系的面索引标记。随后采用分层方式构建整体标记序列：首先利用上述标记构建几何块（即面与边），继而进行几何块序列化。最终，我们为完整B-rep装配出整体序列表示。此外，我们构建了基于Transformer的自回归模型，该模型采用仅含解码器的多层架构与因果掩码机制，通过下一标记预测学习整体标记序列的分布规律。实验表明BrepARG实现了最先进的性能表现。本研究验证了将B-rep表示为整体标记序列的可行性，为B-rep生成开辟了新的研究方向。