轻量图网络自动识别复杂倒角，精度高且模型小。
FilletRec: A Lightweight Graph Neural Network with Intrinsic Features for Automated Fillet Recognition
- 用内在几何特征建模，不依赖姿态变化
- 在复杂倒角上准确率超现有方法，参数量仅1/50
- 适合需要高效建模的工业CAD自动化场景
自动化识别与简化CAD模型中的倒角特征对CAE分析至关重要，但仍是开放挑战。传统规则方法鲁棒性差，现有深度学习模型因通用设计和训练数据不足，在复杂倒角上泛化能力弱、精度低。为此，本文提出端到端的数据驱动框架FillectRec。首先构建并发布大规模、多样化的倒角识别基准数据集以弥补数据不足。基于此，提出轻量图神经网络FillectRec，其核心是利用姿态不变的内在几何特征（如曲率），学习更本质的几何模式，实现对复杂拓扑结构的高精度识别。实验表明，FillectRec在准确率和泛化能力上均超越当前最优方法，参数量仅为基线模型的0.2%-5.4%，体现高效率。最终，通过集成有效的几何简化算法，实现从识别到简化的全自动流程。
原文摘要 · Abstract (English)
Automated recognition and simplification of fillet features in CAD models is critical for CAE analysis, yet it remains an open challenge. Traditional rule-based methods lack robustness, while existing deep learning models suffer from poor generalization and low accuracy on complex fillets due to their generic design and inadequate training data. To address these issues, this paper proposes an end-to-end, data-driven framework specifically for fillet features. We first construct and release a large-scale, diverse benchmark dataset for fillet recognition to address the inadequacy of existing data. Based on it, we propose FilletRec, a lightweight graph neural network. The core innovation of this network is its use of pose-invariant intrinsic geometric features, such as curvature, enabling it to learn more fundamental geometric patterns and thereby achieve high-precision recognition of complex geometric topologies. Experiments show that FilletRec surpasses state-of-the-art methods in both accuracy and generalization, while using only 0.2\%-5.4\% of the parameters of baseline models, demonstrating high model efficiency. Finally, the framework completes the automated workflow from recognition to simplification by integrating an effective geometric simplification algorithm.
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