论文标题
基于相似性距离的平面图探索框架
Floor Plan Exploration Framework Based on Similarity Distances
论文作者
论文摘要
计算平面图之间相似之处的计算方法可以帮助建筑师探索大型数据集中的平面图，以避免重复设计并搜索满足其需求的现有计划。最近，LayoutGMN提供了最先进的性能，用于计算平面图之间的相似性得分。但是，LayoutGMN的高计算成本使其不适合上述应用程序。在本文中，我们通过将平面图投影到一个常见的低维（例如三个）数据空间中来大大减少了LayoutGMN计算出的结果所需的时间。该投影是通过优化欧几里得距离的平面平面图的坐标来完成的，从而模仿了其最初由LayoutGMN计算的相似性得分。定量和定性评估表明，我们的结果与原始布局相似性分数的分布相匹配。用户研究表明，我们的相似性结果在很大程度上与人类的期望相匹配。
Computational methods to compute similarities between floor plans can help architects explore floor plans in large datasets to avoid duplication of designs and to search for existing plans that satisfy their needs. Recently, LayoutGMN delivered state-of-the-art performance for computing similarity scores between floor plans. However, the high computational costs of LayoutGMN make it unsuitable for the aforementioned applications. In this paper, we significantly reduced the times needed to query results computed by LayoutGMN by projecting the floor plans into a common low-dimensional (e.g., three) data space. The projection is done by optimizing for coordinates of floor plans with Euclidean distances mimicking their similarity scores originally calculated by LayoutGMN. Quantitative and qualitative evaluations show that our results match the distributions of the original LayoutGMN similarity scores. User study shows that our similarity results largely match human expectations.
