A building information model for pipes already in place is essential in maintenance, for example, mending, reconstruction, and modernizing. However, current point cloud construction methods are not suited to complex piping systems, and many point cloud merging methods perform poorly in complex piping environments. To provide critical functions for constructing pipe point clouds from digital photogrammetry, a feature type selection network (FTSNet) is proposed. First, a digital photogrammetry method combining FTSNet with handcrafted features is proposed. Second, a piping data set of various piping scenes is constructed and evaluated using the developed data capturing device. A training data set and the corresponding network output categories are determined according to pose-estimation performance using different image feature types. Finally, experiments conducted on the final test data set indicate that, together, digital photogrammetry and FTSNet can improve accuracy, flexibility, and processing speed.

中文翻译：

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管道数字摄影测量中的自动特征类型选择

现有管道的建筑信息模型对于维护（例如修补、重建和现代化）至关重要。然而，目前的点云构建方法并不适用于复杂的管道系统，许多点云合并方法在复杂的管道环境中表现不佳。为了提供从数字摄影测量构建管道点云的关键功能，提出了一种特征类型选择网络（FTSNet）。首先，提出了一种将 FTSNet 与手工特征相结合的数字摄影测量方法。其次，使用开发的数据捕获设备构建和评估各种管道场景的管道数据集。根据使用不同图像特征类型的姿态估计性能确定训练数据集和相应的网络输出类别。最后，