  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.
