Discovering Design Concepts for CAD Sketches

Yuezhi Yang, Hao Pan

Keywords: CAD sketch graph, program library induction, neural-symbolic learning, auto-completion

TL;DR: We present an end-to-end approach to learn inductive concepts as modular program libraries from CAD sketch graphs.

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.

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/arxiv:2210.14451/code)