Discovering Design Concepts for CAD Sketches

TL;DR

This paper presents a learning-based method to automatically discover and generate design concepts in CAD sketches, enhancing understanding and automation in parametric design.

Contribution

It introduces a dual implicit-explicit representation for modular concept detection and generation, enabling end-to-end learning from raw sketches.

Findings

Successfully learned modular design concepts from large CAD sketch dataset.

Enabled automatic interpretation of design intent.

Improved CAD sketch auto-completion capabilities.

Abstract

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.