PP-Brep：基于混合图表示的少样本 B-rep 分类
PP-Brep: Few-Shot B-rep Classification with Hybrid Graph Representation
把 B-rep 的局部拓扑图、全局平行图与区域关联超图组合成混合图，再用结构感知图提示做少样本分类，为 CAD/3D 几何的图表示学习与提示式迁移提供了可复用范式。
原文依据 we propose a hybrid graph representation and a pre-training and graph prompt framework for B-rep few-shot classification
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论在工业场景中，三维 CAD 模型的分类对高效制造至关重要。然而，标注 CAD 模型的有限可得性，给少样本零件分类场景下的快速适配带来了障碍。本文提出一种混合图表示，以及一个用于 B-rep 少样本分类的预训练与图提示框架。具体而言，混合图表示通过构建局部拓扑图、全局平行图和区域关联超图，捕获 B-rep 模型全面且多层的结构信息。随后，分层图网络将部件级结构与混合图中的拓扑细节相融合。强化增强的对比预训练产生稳健的通用表示，而原位扰动则缩短训练时间。结构感知图提示最终产生节点特定的线索，使得无需大量微调即可实现少样本 B-rep 零件分类。在 TraceParts-11 和 FabWave-31 数据集上的实验表明，本方法优于现有的通用方法。本工作为少样本 B-rep 零件分类提供了一种高效且最优的解决方案。
英文原文摘要 1223 字符
In industrial settings, classification of 3D CAD models are critical for efficient manufacturing. However, the limited availability of annotated CAD models presents an obstacle to achieving rapid adaptation in few-shot part classification scenarios. In this paper, we propose a hybrid graph representation and a pre-training and graph prompt framework for B-rep few-shot classification. Specifically, hybrid graph representation captures comprehensive and multi-level structural information of B-rep models by constructing local topology graph, global parallel graph and regional association hypergraph. A hierarchical graph network then fuses component-level structures with topological details in the hybrid graph. Reinforcement-augmented contrastive pre-training produces robust universal representations while in-place perturbation reduces training time. Structure-aware graph prompts finally produce node-specific cues, enabling few-shot B-rep part classification without heavy fine-tuning. Experiments on the TraceParts-11and FabWave-31 datasets show that our method outperforms existing general-purpose approaches. This work provides an efficient and state-of-the-art solution for few-shot B-rep part classification.