BRepMAE：基于加工特征识别的掩码自监督学习框架
BRepMAE: Self-Supervised Masked BRep Autoencoders for Machining Feature Recognition
AI总结：
BRepMAE通过掩码自监督学习框架，利用gAAG表示实现CAD模型加工特征识别，微调后在少量数据下显著提升识别性能。
摘要：
我们提出了一种名为BRepMAE的掩码自监督学习框架，用于自动提取输入计算机辅助设计（CAD）模型的有价值表示，以识别其加工特征。通过几何属性邻接图（gAAG）表示进行表征学习，该表示源自边界表示（BRep）。自监督网络是一种关注重建BRep面片几何和属性的掩码图自动编码器（MAE），而非图结构。预训练后，我们进一步微调包含编码器和任务特定分类网络的网络，用于加工特征识别（MFR）。在实验中，我们的微调网络仅使用少量数据（例如训练数据的0.1%）即可实现高识别率，显著增强了在实际（或私有）场景中数据有限时的实用性。与其它MFR方法相比，我们的微调网络在相同训练数据量下实现了显著的识别率提升，尤其是在训练样本数量有限时。
英文摘要：
We propose a masked self-supervised learning framework, called BRepMAE, for automatically extracting a valuable representation of the input computer-aided design (CAD) model to recognize its machining features. Representation learning is conducted on a large-scale, unlabeled CAD model dataset using the geometric Attributed Adjacency Graph (gAAG) representation, derived from the boundary representation (BRep). The self-supervised network is a masked graph autoencoder (MAE) that focuses on reconstructing geometries and attributes of BRep facets, rather than graph structures. After pre-training, we fine-tune a network that contains both the encoder and a task-specific classification network for machining feature recognition (MFR). In the experiments, our fine-tuned network achieves high recognition rates with only a small amount of data (e.g., 0.1% of the training data), significantly enhancing its practicality in real-world (or private) scenarios where only limited data is available. Compared with other MFR methods, our fine-tuned network achieves a significant improvement in recognition rate with the same amount of training data, especially when the number of training samples is limited.
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。
