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<p>With the widespread application of CAD technology in the industrial manufacturing sector, the efficient retrieval of target models has become a critical research topic. Despite the outstanding performance of traditional supervised retrieval methods, their reliance on large amounts of labeled data significantly limits practical applications. Data labeling is not only time-consuming and costly but also difficult to ensure accuracy and consistency. To tackle this issue, this paper introduces CADGCL, an unsupervised method for retrieving CAD models based on boundary representations. The proposed method transforms CAD models represented by boundary representations into B-rep attributed graphs that integrate geometric information and topological structures. Graph contrastive learning facilitates unsupervised CAD model retrieval. To overcome the limitations of traditional GCL methods in data augmentation and negative sampling, two novel strategies are introduced: an edge perturbation strategy based on Edge Betweenness Centrality and a negative sampling strategy based on the Beta Mixture Model. These strategies effectively improve the performance of contrastive learning. Experimental results show that the proposed method outperforms existing approaches in mAP and F1 scores under unsupervised scenarios, validating its potential for applications in industrial manufacturing.</p>

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CADGCL: unsupervised retrieval of CAD models via boundary representations

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月28日

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  • Feiwei Qin,
  • Liangzhe Zhu,
  • Zijian Xu,
  • Meie Fang,
  • Ping Li

摘要

With the widespread application of CAD technology in the industrial manufacturing sector, the efficient retrieval of target models has become a critical research topic. Despite the outstanding performance of traditional supervised retrieval methods, their reliance on large amounts of labeled data significantly limits practical applications. Data labeling is not only time-consuming and costly but also difficult to ensure accuracy and consistency. To tackle this issue, this paper introduces CADGCL, an unsupervised method for retrieving CAD models based on boundary representations. The proposed method transforms CAD models represented by boundary representations into B-rep attributed graphs that integrate geometric information and topological structures. Graph contrastive learning facilitates unsupervised CAD model retrieval. To overcome the limitations of traditional GCL methods in data augmentation and negative sampling, two novel strategies are introduced: an edge perturbation strategy based on Edge Betweenness Centrality and a negative sampling strategy based on the Beta Mixture Model. These strategies effectively improve the performance of contrastive learning. Experimental results show that the proposed method outperforms existing approaches in mAP and F1 scores under unsupervised scenarios, validating its potential for applications in industrial manufacturing.