Engineering Sketch Generation for Computer-Aided Design
arXiv:2104.09621
Abstract
Engineering sketches form the 2D basis of parametric Computer-Aided Design (CAD), the foremost modeling paradigm for manufactured objects. In this paper we tackle the problem of learning based engineering sketch generation as a first step towards synthesis and composition of parametric CAD models. We propose two generative models, CurveGen and TurtleGen, for engineering sketch generation. Both models generate curve primitives without the need for a sketch constraint solver and explicitly consider topology for downstream use with constraints and 3D CAD modeling operations. We find in our perceptual evaluation using human subjects that both CurveGen and TurtleGen produce more realistic engineering sketches when compared with the current state-of-the-art for engineering sketch generation.
arXiv commentThe 1st Workshop on Sketch-Oriented Deep Learning @ CVPR 2021
References in corpus (9)
- Supervised Fitting of Geometric Primitives to 3D Point Clouds
- Deep Vectorization of Technical Drawings
- UCSG-Net -- Unsupervised Discovering of Constructive Solid Geometry Tree
- SketchGraphs: A Large-Scale Dataset for Modeling Relational Geometry in Computer-Aided Design
- DeepSpline: Data-Driven Reconstruction of Parametric Curves and Surfaces
- LayoutGAN: Generating Graphic Layouts with Wireframe Discriminators
- Deep Learning for Free-Hand Sketch: A Survey
- Fusion 360 Gallery: A Dataset and Environment for Programmatic CAD Construction from Human Design Sequences
- Learning Manifold Patch-Based Representations of Man-Made Shapes