VectorGraphNET: Graph Attention Networks for Accurate Segmentation of Complex Technical Drawings

TL;DR

This paper presents VectorGraphNET, a graph attention network-based method for precise line segmentation in complex technical drawings, outperforming existing techniques in accuracy and scalability.

Contribution

The paper introduces a novel vector-based graph attention transformer approach that achieves state-of-the-art segmentation accuracy with lower computational requirements.

Findings

Achieves superior weighted F1 score on public datasets.

Offers scalable and less GPU-intensive analysis of technical drawings.

Improves accuracy over existing vision-based and vector-based methods.

Abstract

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.