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融合规则和数据驱动的展厅自动空间规划与布局生成

Rule and Data-driven for Automatic Exhibition Hall Space Planning and Layout Generation

  • 摘要:   本文提出了一种融合规则和数据驱动的展厅空间自动规划与布局生成方法. 首先, 根据展厅设计规则对指定空间进行划分, 并通过美化和连接等方式对空间进行优化. 接着, 基于空间关系计算展厅空间视觉显著度, 利用优先队列对展品选择合适的位置和摆放方式. 然后,采用规则生成的数据作为数据集, 以区域中心布局搜索结果作为输入, 结合语义预测网络和拓扑预测网络模块进行多样化的空间结构生成. 最后, 通过预测模型按重要度递减的顺序确定展品的最佳显著位置, 实现虚拟展厅空间布局的自动化生成. 实验结果表明, 该方法能够适应不同展厅和画作数量, 生成具有良好视觉效果的虚拟展厅空间布局.

     

    Abstract:   This paper introduces a method that combines rule and data-driven approaches for the automatic planning and layout generation of exhibition hall spaces. Firstly, the designated space is divided based on exhibition hall design rules and optimized through beautification and connection methods. Next, the visual saliency of spaces is calculated using spatial relationships, and a priority queue is utilized to select appropriate positions and placement methods for exhibits. Then, by using rule-generated data as a dataset and incorporating semantic prediction along with a topological prediction network, diverse spatial structures are generated. Finally, a predictive model is employed to determine the optimal salient positions of exhibits in descending order of importance, enabling the automated generation of space layouts. Experimental results demonstrate the method is able to adapt to different exhibition halls and quantities of paintings, generating exhibition hall space layouts with excellent visual effects.

     

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