CAD (Computer-Aided Design) plays a crucial role in mechanical industry, where large numbers of similar-shaped CAD parts are often created. Efficiently reusing these parts is key to reducing design and production costs for enterprises. Retrieval systems are vital for achieving CAD reuse, but the complex shapes of CAD models are difficult to accurately describe using text or keywords, making traditional retrieval methods ineffective. While existing representation learning approaches have been developed for CAD, manually labeling similar samples in these methods is expensive. Additionally, CAD models' unique parameterized data structure presents challenges for applying existing 3D shape representation learning techniques directly. In this work, we propose GC-CAD, a self-supervised contrastive graph neural network-based method for mechanical CAD retrieval that directly models parameterized CAD raw files. GC-CAD consists of two key modules: structure-aware representation learning and contrastive graph learning framework. The method leverages graph neural networks to extract both geometric and topological information from CAD models, generating feature representations. We then introduce a simple yet effective contrastive graph learning framework approach, enabling the model to train without manual labels and generate retrieval-ready representations. Experimental results on four datasets including human evaluation demonstrate that the proposed method achieves significant accuracy improvements and up to 100 times efficiency improvement over the baseline methods.

翻译：CAD（计算机辅助设计）在机械工业中扮演着关键角色，行业中常常产生大量形状相似的CAD零件。高效复用这些零件是企业降低设计与生产成本的关键。检索系统对于实现CAD复用至关重要，但CAD模型复杂的几何形状难以用文本或关键词准确描述，导致传统检索方法效果不佳。尽管已有针对CAD的表征学习方法被提出，但这些方法中手动标注相似样本的成本高昂。此外，CAD模型独特的参数化数据结构对直接应用现有的三维形状表征学习技术构成了挑战。本研究提出GC-CAD，一种基于自监督对比图神经网络的机械CAD检索方法，可直接对参数化CAD原始文件进行建模。GC-CAD包含两个核心模块：结构感知表征学习与对比图学习框架。该方法利用图神经网络从CAD模型中提取几何与拓扑信息，生成特征表示。我们进而引入一种简洁而有效的对比图学习框架，使模型能够在无需人工标注的情况下进行训练，并生成可直接用于检索的表示。在包含人工评估的四个数据集上的实验结果表明，所提方法相比基线方法实现了显著的精度提升，且检索效率最高可提升100倍。