SketchGraphs: A Large-Scale Dataset for Modeling Relational Geometry in Computer-Aided Design
This dataset facilitates research in machine learning for CAD design, potentially reducing design time and enabling new workflows, though it is incremental as it builds on existing data extraction methods.
The authors tackled the problem of modeling relational geometry in parametric CAD by introducing SketchGraphs, a large-scale dataset of 15 million sketches extracted from real-world CAD models, and established benchmarks for generative modeling and conditional constraint generation.
Parametric computer-aided design (CAD) is the dominant paradigm in mechanical engineering for physical design. Distinguished by relational geometry, parametric CAD models begin as two-dimensional sketches consisting of geometric primitives (e.g., line segments, arcs) and explicit constraints between them (e.g., coincidence, perpendicularity) that form the basis for three-dimensional construction operations. Training machine learning models to reason about and synthesize parametric CAD designs has the potential to reduce design time and enable new design workflows. Additionally, parametric CAD designs can be viewed as instances of constraint programming and they offer a well-scoped test bed for exploring ideas in program synthesis and induction. To facilitate this research, we introduce SketchGraphs, a collection of 15 million sketches extracted from real-world CAD models coupled with an open-source data processing pipeline. Each sketch is represented as a geometric constraint graph where edges denote designer-imposed geometric relationships between primitives, the nodes of the graph. We demonstrate and establish benchmarks for two use cases of the dataset: generative modeling of sketches and conditional generation of likely constraints given unconstrained geometry.