基于B-表示的自回归生成：整体令牌序列表示
AutoRegressive Generation with B-rep Holistic Token Sequence Representation
AI总结：
本文提出BrepARG，首次将B-表示的几何与拓扑转化为整体令牌序列，通过自回归架构实现B-表示生成，实验表明其达到最先进的性能。
摘要：
先前的B-表示表示和生成方法依赖于基于图的表示，通过解耦的计算管道分离几何和拓扑特征，从而排除了序列生成框架如transformer架构的应用。本文提出BrepARG，首次将B-表示的几何和拓扑编码为整体令牌序列表示，实现基于序列的B-表示生成。具体而言，BrepARG将B-表示编码为三种令牌：几何和位置令牌表示几何特征，面索引令牌表示拓扑。然后构建层次化的整体令牌序列，首先通过上述令牌构建几何块（即面和边），然后进行几何块序列化。最后，将整体序列表示组装为整个B-表示。我们还构建了一个基于transformer的自回归模型，通过下一个令牌预测学习整体令牌序列的分布，使用多层解码器-only架构和因果掩码。实验表明，BrepARG实现了最先进的性能。BrepARG验证了将B-表示表示为整体令牌序列的可行性，为B-表示生成开辟了新方向。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
