用Transformer识别B-rep模型的几何特征，精度领先。
BRepFormer: Transformer-Based B-rep Geometric Feature Recognition
- 基于Transformer架构融合几何与拓扑特征
- 在3个数据集上达到当前最佳准确率
- 适用于复杂CAD模型的工业级特征识别
识别B-rep模型中的几何特征是多媒体内容检索的关键技术，广泛应用于智能制造领域。然而，以往研究多聚焦于加工特征识别（MFR），难以有效捕捉复杂几何特征的拓扑与几何特性。本文提出BRepFormer，一种基于Transformer的新模型，可同时识别加工特征与复杂CAD模型的几何特征。该模型对几何与拓扑特征进行编码与融合，并利用Transformer结构实现特征传播，通过识别头完成特征分类。每次Transformer迭代中，引入结合边特征与拓扑特征的偏置项，强化各面的几何约束。此外，本文还构建了包含20,000个B-rep模型的复杂B-rep特征数据集（CBF），更贴近工业应用。实验表明，BRepFormer在MFInstSeg、MFTRCAD及CBF数据集上均取得当前最优性能。
原文摘要 · Abstract (English)
Recognizing geometric features on B-rep models is a cornerstone technique for multimedia content-based retrieval and has been widely applied in intelligent manufacturing. However, previous research often merely focused on Machining Feature Recognition (MFR), falling short in effectively capturing the intricate topological and geometric characteristics of complex geometry features. In this paper, we propose BRepFormer, a novel transformer-based model to recognize both machining feature and complex CAD models' features. BRepFormer encodes and fuses the geometric and topological features of the models. Afterwards, BRepFormer utilizes a transformer architecture for feature propagation and a recognition head to identify geometry features. During each iteration of the transformer, we incorporate a bias that combines edge features and topology features to reinforce geometric constraints on each face. In addition, we also proposed a dataset named Complex B-rep Feature Dataset (CBF), comprising 20,000 B-rep models. By covering more complex B-rep models, it is better aligned with industrial applications. The experimental results demonstrate that BRepFormer achieves state-of-the-art accuracy on the MFInstSeg, MFTRCAD, and our CBF datasets.
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