DAVINCI: A Single-Stage Architecture for Constrained CAD Sketch Inference


Ahmet Serdar Karadeniz (University of Luxemburg), Dimitrios Mallis (University of Luxemburg), Nesryne Mejri (University of Luxembourg), Kseniya Cherenkova (University of Luxemburg), Anis Kacem (University of Luxemburg), Djamila Aouada (University of Luxemburg)
The 35th British Machine Vision Conference
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Abstract

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.

Citation

@inproceedings{Karadeniz_2024_BMVC,
author    = {Ahmet Serdar Karadeniz and Dimitrios Mallis and Nesryne Mejri and Kseniya Cherenkova and Anis Kacem and Djamila Aouada},
title     = {DAVINCI: A Single-Stage Architecture for Constrained CAD Sketch Inference},
booktitle = {35th British Machine Vision Conference 2024, {BMVC} 2024, Glasgow, UK, November 25-28, 2024},
publisher = {BMVA},
year      = {2024},
url       = {https://papers.bmvc2024.org/0486.pdf}
}


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