ESTIMATION OF THE LONGITUDINAL DIRECTION OF BRIDGES IN POINT CLOUDS USING DEEP LEARNING POSE ESTIMATION METHODS

Abstract

3D modeling technology using point clouds needs further development to improve the efficiency of operation and maintenance for civil engineering structures. In this study, we propose a deep learning model and a subsampling method to estimate the longitudinal direction of bridges, which is necessary information to align point clouds in 3D modeling. Our deep learning model was developed by applying the pose estimation approach of the past study. Our subsampling method can generate datasets for deep learning as training data considering disturbance of point clouds with actual site conditions. The results estimated using our method were compared with that estimated using the principal component analysis, which is one of the conventional methods. Our method yielded higher accuracy than the principal component analysis even when handling point clouds with a large amount of noise and missing points. This means that our method has robustness for estimation of the longitudinal direction of bridges.