Point cloud of existing bridges provides important applications in their maintenance and management, such as to the three‐dimensional (3D) model creation. However, point cloud data acquired in actual bridges are caused missing parts due to occlusions and limitations in sensor placements. This study proposes a learning method to realize the point cloud completion of such structure: the component‐wise learning combining the initial weight transfer, to overcome the difficulty particularly found in the bridge structures, where a whole structure consists of multiple and various components. The learning method was developed and verified using point cloud data acquired in an actual concrete bridge based on the point cloud completion performance of three significant deep learning models. The effectiveness and applicability of the proposed method were shown in that it improved performances in component level in applying it to the bridge point cloud completion by the multiple deep learning models, respectively.

中文翻译：

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使用深度学习完成桥梁结构的组件级点云

现有桥梁的点云在其维护和管理中提供了重要的应用，例如三维 (3D) 模型创建。然而，由于传感器放置的遮挡和限制，在实际桥梁中获取的点云数据会导致部分缺失。本研究提出了一种实现此类结构的点云补全的学习方法：结合初始权重转移的逐组件学习，以克服桥梁结构中特别存在的困难，其中整个结构由多个不同的组件组成。基于三个重要深度学习模型的点云完成性能，使用实际混凝土桥梁中获取的点云数据开发和验证了学习方法。该方法的有效性和适用性表现在，通过多种深度​​学习模型将其应用于桥梁点云补全，分别提高了组件级别的性能。