一次完成草图参数化与约束推理，提升CAD绘图精度
DAVINCI: A Single-Stage Architecture for Constrained CAD Sketch Inference
- 单阶段联合学习参数与约束，减少误差累积
- 在SketchGraphs上达顶尖水平，手绘图也表现优异
- 用约束保持变换增强数据，仅需0.1%标注数据就有效
本文提出DAVINCI，一种统一的单阶段架构，可直接从栅格草图图像中推断计算机辅助设计（CAD）草图的参数与约束。通过联合学习两项输出，DAVINCI减少误差传播，显著提升约束型CAD草图推理性能。在大规模SketchGraphs数据集上，DAVINCI实现当前最佳效果，适用于精确与手绘栅格草图。为降低对大规模标注数据的依赖，本文探索了CAD草图增强策略，提出约束保持变换（CPTs），即对草图参数化构件进行随机排列但保持其约束关系。该数据增强方法使DAVINCI在仅使用0.1% SketchGraphs数据训练时仍能取得合理性能。此外，本文还发布新版SketchGraphs数据集——CPTSketchGraphs，包含8000万条经CPT增强的草图，为未来CAD草图研究提供丰富资源。
原文摘要 · Abstract (English)
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.
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