We introduce a novel self-supervised learning framework that automatically learns representations from input computer-aided design (CAD) models for downstream tasks, including part classification, modeling segmentation, and machining feature recognition. To train our network, we construct a large-scale, unlabeled dataset of boundary representation (BRep) models. The success of our algorithm relies on two keycomponents. The first is a masked graph autoencoder that reconstructs randomly masked geometries and attributes of BReps for representation learning to enhance the generalization. The second is a hierarchical graph Transformer architecture that elegantly fuses global and local learning by a cross-scale mutual attention block to model long-range geometric dependencies and a graph neural network block to aggregate local topological information. After training the autoencoder, we replace its decoder with a task-specific network trained on a small amount of labeled data for downstream tasks. We conduct experiments on various tasks and achieve high performance, even with a small amount of labeled data, demonstrating the practicality and generalizability of our model. Compared to other methods, our model performs significantly better on downstream tasks with the same amount of training data, particularly when the training data is very limited.

翻译：我们提出了一种新颖的自监督学习框架，能够自动从输入的计算机辅助设计（CAD）模型中学习表征，以支持下游任务，包括零件分类、建模分割和加工特征识别。为了训练我们的网络，我们构建了一个大规模、无标签的边界表示（BRep）模型数据集。我们算法的成功依赖于两个关键组件。首先是一个掩码图自编码器，它通过重建随机掩码的BRep几何形状和属性来进行表征学习，以增强泛化能力。其次是一个层次图Transformer架构，该架构通过跨尺度互注意力模块来建模长程几何依赖关系，以及通过图神经网络模块来聚合局部拓扑信息，从而优雅地融合了全局与局部学习。在训练自编码器后，我们将其解码器替换为针对下游任务、使用少量标签数据训练的任务特定网络。我们在多种任务上进行了实验，即使使用少量标签数据也取得了高性能，这证明了我们模型的实用性和泛化能力。与其他方法相比，在相同训练数据量下，我们的模型在下游任务上表现显著更优，尤其是在训练数据非常有限的情况下。