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.

翻译：本研究提出DAVINCI，这是一种直接从栅格草图图像进行单阶段计算机辅助设计（CAD）草图参数化与约束推断的统一架构。通过联合学习这两个输出，DAVINCI最大限度地减少了误差累积，并提升了约束CAD草图推断的性能。值得注意的是，DAVINCI在大规模SketchGraphs数据集上取得了最先进的结果，证明了其在精确CAD草图和手绘栅格CAD草图上的有效性。为了降低DAVINCI对大规模标注数据集的依赖，我们探索了CAD草图增强的有效性。我们引入了约束保持变换（CPTs），即对CAD草图的参数化基元进行随机排列，同时保持其约束不变。这种数据增强策略使得DAVINCI在仅使用SketchGraphs数据集的0.1%进行训练时，也能达到合理的性能。此外，本研究贡献了一个新版本的SketchGraphs数据集，该数据集通过CPTs进行了增强。新引入的CPTSketchGraphs数据集包含8000万个经过CPT增强的草图，从而为CAD草图领域的未来研究提供了丰富的资源。