Application of deep learning methods treating convolutional features of local geometry to point cloud analysis of civil infrastructures

Abstract

Analysis technology for point clouds needs further development to improve the efficiency of maintenance and modeling for civil infrastructures. In this study, we proposed a deep learning model handling convolutional features of local geometry to obtain information necessary for structural modeling from point clouds of civil infrastructures. The proposed method can be adopted for high-resolution point clouds of civil infrastructures by tuning the convolution process. The longitudinal direction of bridges composed of point clouds was estimated as a benchmark task, and it was confirmed that the proposed method improved the estimation accuracy. This means that the proposed method treating convolutional features of local geometry can be applied for accurate estimation of point clouds.