The design and analysis of Computer-Aided Design (CAD) sketches play a crucial role in industrial product design, primarily involving CAD primitives and their inter-primitive constraints. To address challenges related to error accumulation in autoregressive models and the complexities associated with self-supervised model design for this task, we propose a two-stage network framework. This framework consists of a primitive network and a constraint network, transforming the sketch analysis task into a set prediction problem to enhance the effective handling of primitives and constraints. By decoupling target types from parameters, the model gains increased flexibility and optimization while reducing complexity. Additionally, the constraint network incorporates a pointer module to explicitly indicate the relationship between constraint parameters and primitive indices, enhancing interpretability and performance. Qualitative and quantitative analyses on two publicly available datasets demonstrate the superiority of this method.

在工业产品设计中，计算机辅助设计（CAD）草图的设计和分析起着关键作用，主要涉及CAD基元及其基元约束。为了解决自回归模型中误差累积和自我监督模型设计的复杂性带来的挑战，我们提出了一个两阶段网络框架。该框架由基元网络和约束网络组成，将草图分析任务转化为一个集合预测问题，以加强对基元和约束的有效处理。通过将目标类型与参数解耦，该模型在减少复杂性的同时增加了灵活性和优化。此外，约束网络还结合了指针模块，明确指示约束参数和基元索引之间的关系，提高了可解释性和性能。对两个公开可用的数据集进行的定性和定量分析证明了该方法的优越性。

CAD草图的参数化基元分析与Vision Transformer