Despite progress in automated bridge point cloud segmentation based on deep learning, challenges persist. For instance, the absence of a public point cloud dataset specifically designed for bridge instances, and the existing bridge point cloud datasets display a lack of diversity in bridge types and inconsistency in component labeling. These factors may hinder the further improvement of accuracy in bridge point cloud segmentation. In this paper, a universal multi‐type bridge point cloud databank, named BrPCD, consisting of a total of 98 point cloud data (PCD; 10 of them are obtained from scanning, and the rest is obtained by data augmentation) from small to long‐span bridges, is established. Additionally, a method for augmenting bridge PCD is proposed, significantly enriching the spatial feature information of bridges within the dataset. Furthermore, based on the introduced data annotation rules, a uniform categorization of semantic labels for bridge components is implemented, enhancing the applicability of our dataset across various semantic segmentation tasks for different types of bridges. A benchmark testing was conducted on the BrPCD using the PointNet model. The segmentation results indicate that the parameters learned through the BrPCD enable accurate segmentation at the level of various types of bridge components. In other words, the BrPCD can function as a universal dataset, applicable for testing various networks aimed at bridge point cloud segmentation.

中文翻译：

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用于数字桥梁理解的桥梁点云数据库


尽管基于深度学习的自动桥接点云分割取得了进展，但挑战仍然存在。例如，缺少专门为桥梁实例设计的公共点云数据集，而现有的桥梁点云数据集表明桥梁类型缺乏多样性，组件标记也不一致。这些因素可能会阻碍桥梁点云分割精度的进一步提高。本文建立了一个名为 BrPCD 的通用多类型桥梁点云数据库，由从小跨度到大跨度桥梁的 98 个点云数据（PCD;其中 10 个是通过扫描获得的，其余的是通过数据增强获得的）组成。此外，该文还提出了一种增强桥梁 PCD 的方法，显著丰富了数据集内桥梁的空间特征信息。此外，基于引入的数据注释规则，实现了桥梁组件语义标签的统一分类，增强了我们的数据集在不同类型的桥梁的各种语义分割任务中的适用性。使用 PointNet 模型对 BrPCD 进行了基准测试。分割结果表明，通过 BrPCD 学习的参数可以在各种类型的桥梁组件级别进行准确分割。换句话说，BrPCD 可以用作通用数据集，适用于测试旨在桥接点云分割的各种网络。