VectorGraphNET: Graph Attention Networks for Accurate Segmentation of Complex Technical Drawings
<|reference_start|>VectorGraphNET: Graph Attention Networks for Accurate Segmentation of Complex Technical Drawings: This paper introduces a new approach to extract and analyze vector data from technical drawings in PDF format. Our method involves converting PDF files into SVG format and creating a feature-rich graph representation, which captures the relationships between vector entities using geometrical information. We then apply a graph attention transformer with hierarchical label definition to achieve accurate line-level segmentation. Our approach is evaluated on two datasets, including the public FloorplanCAD dataset, which achieves state-of-the-art results on weighted F1 score, surpassing existing methods. The proposed vector-based method offers a more scalable solution for large-scale technical drawing analysis compared to vision-based approaches, while also requiring significantly less GPU power than current state-of-the-art vector-based techniques. Moreover, it demonstrates improved performance in terms of the weighted F1 (wF1) score on the semantic segmentation task. Our results demonstrate the effectiveness of our approach in extracting meaningful information from technical drawings, enabling new applications, and improving existing workflows in the AEC industry. Potential applications of our approach include automated building information modeling (BIM) and construction planning, which could significantly impact the efficiency and productivity of the industry.<|reference_end|>
@article{carrara2024vectorgraphnet:,
    title={VectorGraphNET: Graph Attention Networks for Accurate Segmentation of
  Complex Technical Drawings},
    author={Andrea Carrara and Stavros Nousias and Andr'e Borrmann},
    journal={arXiv preprint arXiv:2410.01336},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.01336},
    primaryClass={cs.CV}
}