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图学学报

图学学报 ›› 2022, Vol. 43 ›› Issue (1): 101-109.DOI: 10.11996/JG.j.2095-302X.2022010101

• 计算机图形学与虚拟现实 • 上一篇    下一篇

基于图卷积网络的 BREP→CSG 转换方法及 其应用研究 

  

  1. 1. 合肥工业大学计算机与信息学院，安徽 合肥 230601；  2. 中国科学院等离子体物理研究所，安徽 合肥 230601；  3. 国家电投集团科学技术研究院有限公司，北京 100033
  • 出版日期:2022-02-28 发布日期:2022-02-16
  • 基金资助:
    国家重点研发计划项目(2017YFB1402200)；安徽省科技攻关计划项目(1604d0802009)；国家自然科学基金项目(61602146) 

Graph convolution network based BREP→CSG conversion method and its application

  1. 1. School of Computer Science and Information Technology, Hefei Anhui 230601, China;  2. Institute of Plasma Physics, Chinese Academy of Sciences, Hefei Anhui 230601, China;  3. State Power Investment Corporation Research Institute, Beijing 100033, China
  • Online:2022-02-28 Published:2022-02-16
  • Supported by:
    National Key Research and Development Program (2017YFB1402200); Scientific and Technical Key Project in Anhui Province (1604d0802009); National Natural Science Foundation of China (61602146)

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摘要： 边界表示法(BREP)和构造实体表示法(CSG)是应用最广泛的 2 种实体表示法，在粒子输运计算辅 助建模等领域对 BREP→CSG 自动转换算法有迫切的需求，但目前最常用的基于分割的 BREP→CSG 转换算法存 在“计算量大、CSG 表达过于复杂”等不足。观察到“拓扑相似的 BREP 模型的 CSG 表达结构类似”，因此提出建 立包含（BREP，CSG）二元组的模型库，对待转换的 BREP 模型，通过从模型库中检索相似模型，进而基于相 似模型的 CSG 表达生成转换结果。该方法一方面可以提高转换速度，另一方面通过优化 CSG 表达，克服了基于 空间分割方法的不足。采用扩展的属性邻接图刻画 BREP 模型的拓扑特征，将模型相似问题看作属性邻接图分类 问题，进而应用图卷积网络(GCN)实现快速模型检索，对属性邻接图的扩展属性也进行了精心设计，以提高模型 检索的准确性。该算法已集成进入自主研发粒子输运可视建模软件 cosVMPT 并使用中国聚变工程实验堆(CFETR) 中的典型复杂部件偏滤器模型进行测试，测试结果展现了该算法的时间有效性和 CSG 结果优越性。

关键词: BREP→CSG 转换, 相似性, 属性邻接图, 图卷积网络, 中国聚变工程实验堆

Abstract: Boundary representation (BREP) and construction solid geometry (CSG) serve as the two most widely employed entity representations. There remains an urgent need for the BREP→CSG automatic conversion algorithm in such fields as particle transport calculation auxiliary modeling. However, the most commonly adopted segmentation-based BREP→CSG conversion algorithm is disadvantageous in “large amount of calculation and too complicated CSG expression”. Through the observation that “the CSG expression structure of the topologically similar BREP model is similar”, it was proposed to establish a model library containing the two tuples BREP and CSG. For the BREP model to be converted, the similar model was retrieved from the model library, and then the conversion result was generated based on the CSG expression of the similar model. On the one hand, this method can improve the conversion speed, and on the other hand, by optimizing the CSG expression, it can overcome the shortcomings of the space-based segmentation method. The extended attribute adjacency graph was applied to the description of the topological characteristics of the BREP model, the model similarity problem was regarded as the attribute adjacency graph classification problem, and then the graph convolutional network (GCN) was utilized to achieve fast model retrieval. The extended attributes of the attribute adjacency graph were also carefully designed to boost the accuracy of model retrieval. The algorithm has been integrated into the self-developed particle transport visual modeling software cosVMPT (COSINE visual modelling of particle transport), and tests were performed using the typical complex component divertor model in China Fusion Engineering Test Reactor (CFETR). The test results show the time validity of the algorithm and the superiority of the CSG results. 

Key words: BREP→CSG conversion, similarity, attribute adjacency graph, graph convolutional network, China Fusion Engineering Test Reactor 

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