In Japan, the deterioration of infrastructure constructed during the period of rapid economic growth has led to serious issues. In response, the Ministry of Land, Infrastructure, Transport and Tourism has adopted the application of BIM/CIM principles, aiming to improve productivity through the utilization of 3D models. For existing structures, it is necessary to create 3D models based on measurement data, and LOD 300 is considered the standard for such 3D models. However, complex structures such as bridges require specialized knowledge and significant effort. Therefore, methods have been developed to automatically classify acquired point cloud data as preprocessing to reduce work costs. Nevertheless, these approaches are insufficient for reproducing the external geometry of objects as defined by LOD 300 and require significant manual work to create training data. Accordingly, this research proposes an efficient training data generation method utilizing 3D models to construct general semantic segmentation models on bridge components.
