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

• URL: http://arxiv.org/abs/2410.01336v1
• Date: Wed, 2 Oct 2024 08:53:20 GMT
• Title: VectorGraphNET: Graph Attention Networks for Accurate Segmentation of Complex Technical Drawings
• Authors: Andrea Carrara, Stavros Nousias, André Borrmann,
• Abstract summary: 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. We then apply a graph attention transformer with hierarchical label definition to achieve accurate line-level segmentation.
• Score: 0.40964539027092917
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

Related papers

• Language Models are Graph Learners [70.14063765424012]
  Language Models (LMs) are challenging the dominance of domain-specific models, including Graph Neural Networks (GNNs) and Graph Transformers (GTs) We propose a novel approach that empowers off-the-shelf LMs to achieve performance comparable to state-of-the-art GNNs on node classification tasks.
  arXiv  Detail & Related papers  (2024-10-03T08:27:54Z)
• SGTR+: End-to-end Scene Graph Generation with Transformer [42.396971149458324]
  Scene Graph Generation (SGG) remains a challenging visual understanding task due to its compositional property. Most previous works adopt a bottom-up, two-stage or point-based, one-stage approach, which often suffers from high time complexity or suboptimal designs. We propose a novel SGG method to address the aforementioned issues, formulating the task as a bipartite graph construction problem.
  arXiv  Detail & Related papers  (2024-01-23T15:18:20Z)
• Gradient Flow of Energy: A General and Efficient Approach for Entity Alignment Decoding [24.613735853099534]
  We introduce a novel, generalized, and efficient decoding approach for EA, relying solely on entity embeddings. Our method optimize the decoding process by minimizing Dirichlet energy, leading to the gradient flow within the graph, to maximize graph homophily. Notably, the approach achieves these advancements with less than 6 seconds of additional computational time.
  arXiv  Detail & Related papers  (2024-01-23T14:31:12Z)
• SimTeG: A Frustratingly Simple Approach Improves Textual Graph Learning [131.04781590452308]
  We present SimTeG, a frustratingly Simple approach for Textual Graph learning. We first perform supervised parameter-efficient fine-tuning (PEFT) on a pre-trained LM on the downstream task. We then generate node embeddings using the last hidden states of finetuned LM.
  arXiv  Detail & Related papers  (2023-08-03T07:00:04Z)
• Component Segmentation of Engineering Drawings Using Graph Convolutional Networks [0.8941624592392744]
  We present a data-driven framework to automate the vectorization and machine interpretation of 2D engineering part drawings. To overcome these challenges, we propose a deep learning based framework that predicts the semantic type of each vectorized component. Results show that our method yields the best performance compared to recent image, and graph-based segmentation methods.
  arXiv  Detail & Related papers  (2022-12-01T05:31:07Z)
• Graph Property Prediction on Open Graph Benchmark: A Winning Solution by Graph Neural Architecture Search [37.89305885538052]
  We design a graph neural network framework for graph classification task by introducing PAS(Pooling Architecture Search) We improve it based on the GNN topology design method F2GNN to further improve the performance of the model in the graph property prediction task. It is proved that the NAS method has high generalization ability for multiple tasks and the advantage of our method in processing graph property prediction tasks.
  arXiv  Detail & Related papers  (2022-07-13T08:17:48Z)
• GraphCoCo: Graph Complementary Contrastive Learning [65.89743197355722]
  Graph Contrastive Learning (GCL) has shown promising performance in graph representation learning (GRL) without the supervision of manual annotations. This paper proposes an effective graph complementary contrastive learning approach named GraphCoCo to tackle the above issue.
  arXiv  Detail & Related papers  (2022-03-24T02:58:36Z)
• Effective and Efficient Graph Learning for Multi-view Clustering [173.8313827799077]
  We propose an effective and efficient graph learning model for multi-view clustering. Our method exploits the view-similar between graphs of different views by the minimization of tensor Schatten p-norm. Our proposed algorithm is time-economical and obtains the stable results and scales well with the data size.
  arXiv  Detail & Related papers  (2021-08-15T13:14:28Z)
• Robust Optimization as Data Augmentation for Large-scale Graphs [117.2376815614148]
  We propose FLAG (Free Large-scale Adversarial Augmentation on Graphs), which iteratively augments node features with gradient-based adversarial perturbations during training. FLAG is a general-purpose approach for graph data, which universally works in node classification, link prediction, and graph classification tasks.
  arXiv  Detail & Related papers  (2020-10-19T21:51:47Z)
• ProGraML: Graph-based Deep Learning for Program Optimization and Analysis [16.520971531754018]
  We introduce ProGraML, a graph-based program representation for machine learning. ProGraML achieves an average 94.0 F1 score, significantly outperforming the state-of-the-art approaches. We then apply our approach to two high-level tasks - heterogeneous device mapping and program classification - setting new state-of-the-art performance in both.
  arXiv  Detail & Related papers  (2020-03-23T20:27:00Z)
• Graph Representation Learning via Graphical Mutual Information Maximization [86.32278001019854]
  We propose a novel concept, Graphical Mutual Information (GMI), to measure the correlation between input graphs and high-level hidden representations. We develop an unsupervised learning model trained by maximizing GMI between the input and output of a graph neural encoder.
  arXiv  Detail & Related papers  (2020-02-04T08:33:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.