Abstract 3D point cloud data can be utilized for site inspection and reverse engineering of building models. However, conventional methods for building element retrieval require a database of 3D CAD or BIM models which are unsuitable for the case of historical buildings without as-planned models or temporary structures that are not in the pre-built model. Thus, this paper proposes a semi-automated method to efficiently retrieve duplicate building elements without these constraints. First, the point cloud is processed with a pre-trained deep feature extractor to generate a 50-dimensional feature vector for each point. Next, the point cloud is segmented through feature clustering and region-growing algorithms, then displayed on a user interface for selection. Lastly, the selected exemplar is provided as input to a peak-finding algorithm to determine positive matches. Experimental results on five different datasets show that the proposed method obtains average rates above 90% for precision and recall.

中文翻译：

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使用深度点级特征从原始点云中检索基于用户示例的建筑元素

摘要 3D 点云数据可用于建筑模型的现场检查和逆向工程。然而，传统的建筑元素检索方法需要 3D CAD 或 BIM 模型的数据库，这不适用于没有按计划模型的历史建筑或不在预建模型中的临时结构的情况。因此，本文提出了一种半自动化方法，可以在没有这些约束的情况下有效地检索重复的建筑元素。首先，用预先训练好的深度特征提取器处理点云，为每个点生成一个 50 维的特征向量。接下来，通过特征聚类和区域增长算法对点云进行分割，然后显示在用户界面上以供选择。最后，所选择的样本作为输入提供给寻峰算法以确定正匹配。在五个不同数据集上的实验结果表明，所提出的方法获得了 90% 以上的准确率和召回率的平均率。