Discovering Design Concepts for CAD Sketches
This work addresses the need for automated concept discovery in CAD design, offering incremental improvements for CAD designers by formalizing implicit patterns.
The paper tackles the problem of discovering recurring modular patterns in CAD sketches by proposing a learning-based approach that uses a dual implicit-explicit representation and separates structure generation from parameter instantiation, demonstrating its effectiveness on a large-scale dataset for applications like design intent interpretation and auto-completion.
Sketch design concepts are recurring patterns found in parametric CAD sketches. Though rarely explicitly formalized by the CAD designers, these concepts are implicitly used in design for modularity and regularity. In this paper, we propose a learning based approach that discovers the modular concepts by induction over raw sketches. We propose the dual implicit-explicit representation of concept structures that allows implicit detection and explicit generation, and the separation of structure generation and parameter instantiation for parameterized concept generation, to learn modular concepts by end-to-end training. We demonstrate the design concept learning on a large scale CAD sketch dataset and show its applications for design intent interpretation and auto-completion.