DAVINCI: A Single-Stage Architecture for Constrained CAD Sketch Inference
This work addresses the challenge of automating CAD sketch interpretation for design and engineering applications, offering a more efficient and data-efficient solution.
The paper tackles the problem of inferring CAD sketch parameters and constraints from raster images with a single-stage architecture called DAVINCI, achieving state-of-the-art results on the SketchGraphs dataset and enabling training with only 0.1% of the data using constraint-preserving augmentations.
This work presents DAVINCI, a unified architecture for single-stage Computer-Aided Design (CAD) sketch parameterization and constraint inference directly from raster sketch images. By jointly learning both outputs, DAVINCI minimizes error accumulation and enhances the performance of constrained CAD sketch inference. Notably, DAVINCI achieves state-of-the-art results on the large-scale SketchGraphs dataset, demonstrating effectiveness on both precise and hand-drawn raster CAD sketches. To reduce DAVINCI's reliance on large-scale annotated datasets, we explore the efficacy of CAD sketch augmentations. We introduce Constraint-Preserving Transformations (CPTs), i.e. random permutations of the parametric primitives of a CAD sketch that preserve its constraints. This data augmentation strategy allows DAVINCI to achieve reasonable performance when trained with only 0.1% of the SketchGraphs dataset. Furthermore, this work contributes a new version of SketchGraphs, augmented with CPTs. The newly introduced CPTSketchGraphs dataset includes 80 million CPT-augmented sketches, thus providing a rich resource for future research in the CAD sketch domain.