AutoBrep：基于统一拓扑和几何的自回归B-Rep生成
AutoBrep: Autoregressive B-Rep Generation with Unified Topology and Geometry
AI总结：
AutoBrep通过统一拓扑和几何标记方案，实现高质量B-Rep自回归生成，提升CAD设计效率与精度。
摘要：
边界表示（B-Rep）是计算机辅助设计（CAD）中定义实体模型的标准数据结构。尽管近年来取得了一定进展，但直接端到端生成具有精确几何和密闭拓扑的B-Rep仍是一个挑战。本文提出了AutoBrep，一种新的Transformer模型，能够自回归地生成高质量且有效的B-Rep。AutoBrep采用了一种统一的标记方案，将B-Rep模型的几何和拓扑特性编码为离散标记的序列。几何原语（即曲面和曲线）被编码为潜在几何标记，其结构关系被定义为特殊的拓扑参考标记。AutoBrep中的序列顺序自然遵循B-Rep面邻接图的广度优先遍历。在推理过程中，相邻的面和边及其拓扑结构会逐步生成。广泛的实验表明，当与下一个标记预测结合时，我们的统一表示在B-Rep生成中具有优势。AutoBrep在质量和密闭性方面优于基线模型。它还能够很好地扩展到复杂的固体，具有良好的保真度和推理速度。我们进一步表明，通过我们的统一标记化，自动完成B-Rep是原生支持的，使用户可控的CAD生成变得可能，只需最小的修改。代码可在https://github.com/AutodeskAILab/AutoBrep上获得。
英文摘要：
The boundary representation (B-Rep) is the standard data structure used in Computer-Aided Design (CAD) for defining solid models. Despite recent progress, directly generating B-Reps end-to-end with precise geometry and watertight topology remains a challenge. This paper presents AutoBrep, a novel Transformer model that autoregressively generates B-Reps with high quality and validity. AutoBrep employs a unified tokenization scheme that encodes both geometric and topological characteristics of a B-Rep model as a sequence of discrete tokens. Geometric primitives (i.e., surfaces and curves) are encoded as latent geometry tokens, and their structural relationships are defined as special topological reference tokens. Sequence order in AutoBrep naturally follows a breadth first traversal of the B-Rep face adjacency graph. At inference time, neighboring faces and edges along with their topological structure are progressively generated. Extensive experiments demonstrate the advantages of our unified representation when coupled with next-token prediction for B-Rep generation. AutoBrep outperforms baselines with better quality and watertightness. It is also highly scalable to complex solids with good fidelity and inference speed. We further show that autocompleting B-Reps is natively supported through our unified tokenization, enabling user-controllable CAD generation with minimal changes. Code is available at https://github.com/AutodeskAILab/AutoBrep.
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。
