Sketch2CAD: Generative Adversarial Network for Automated Conversion of Hand-Drawn Sketches to Parametric CAD Models

Abstract

This paper addresses the labor-intensive process of converting imprecise hand-drawn sketches into precise, parametric CAD sketches. We present Sketch2CAD, a novel deep learning framework that leverages generative adversarial networks (GANs) to automate this conversion. Our approach consists of two main stages: first, a sketch correction module transforms freehand sketches into clean, standardized CAD-like sketches; second, a semantic segmentation module parses the generated sketches to identify and classify geometric primitives (lines, circles, arcs, points). We further introduce an optimized post-processing algorithm that extracts parametric primitives and infers geometric constraints from the segmentation results, enabling direct integration with commercial CAD software. Extensive experiments demonstrate that our method significantly outperforms state-of-the-art approaches in both primitive accuracy (94.56%) and constraint recognition. This work provides a robust solution that reduces manual effort in CAD drafting while maintaining engineering precision, particularly suitable for robotics applications requiring rapid prototyping and design iteration.