Parametric Primitive Analysis of CAD Sketches with Vision Transformer
This addresses challenges in industrial product design for CAD sketch analysis, but it is incremental as it builds on existing methods with specific improvements.
The paper tackles the problem of analyzing CAD sketches by proposing a two-stage network framework to handle primitives and constraints, reducing error accumulation and complexity, and demonstrates superiority on two datasets with qualitative and quantitative analyses.
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.