Geometric Deep Learning techniques have become a transformative force in the field of Computer-Aided Design (CAD), and have the potential to revolutionize how designers and engineers approach and enhance the design process. By harnessing the power of machine learning-based methods, CAD designers can optimize their workflows, save time and effort while making better informed decisions, and create designs that are both innovative and practical. The ability to process the CAD designs represented by geometric data and to analyze their encoded features enables the identification of similarities among diverse CAD models, the proposition of alternative designs and enhancements, and even the generation of novel design alternatives. This survey offers a comprehensive overview of learning-based methods in computer-aided design across various categories, including similarity analysis and retrieval, 2D and 3D CAD model synthesis, and CAD generation from point clouds. Additionally, it provides a complete list of benchmark datasets and their characteristics, along with open-source codes that have propelled research in this domain. The final discussion delves into the challenges prevalent in this field, followed by potential future research directions in this rapidly evolving field.

翻译：几何深度学习技术已成为计算机辅助设计领域的一股变革力量，并有望彻底改变设计师和工程师优化设计流程的方式。通过利用基于机器学习的方法，CAD设计师可以优化工作流程，节省时间和精力，做出更明智的决策，并创建兼具创新性和实用性的设计。处理以几何数据表示的CAD设计并分析其编码特征的能力，使得识别不同CAD模型间的相似性、提出替代设计方案与改进方案，甚至生成全新设计变体成为可能。本综述全面概述了计算机辅助设计中基于学习方法的多类应用，包括相似性分析与检索、二维与三维CAD模型合成，以及从点云生成CAD模型。此外，本文提供了推动该领域研究的基准数据集及其特征的完整列表，以及开源代码。最后，讨论部分深入剖析了该领域当前面临的挑战，并展望了这一快速发展领域的潜在未来研究方向。