Discrimination of structures in a plant facility based on projected image created from colored 3D point cloud data

今渕 貴志; 川端 邦明

Proceedings of 2023 IEEE/SICE International Symposium on System Integration (SII 2023) (Internet), p.396 - 400, 2023/01

This paper describes a method for discrimination of structures in a plant facility by deep learning based on projected images created from a colored 3D point cloud data using a virtual camera system. In order to promote safe and secure decommissioning works, it is important to recognize a radiation condition in the workspace via calculation based on the measured sensor data. In our previous work, we proposed a structure discrimination method by 3D semantic segmentation network to obtain clues required for radiation dose simulation: shape regions for creating 3D shape model and structural category labels for assigning material information. However, in the evaluation, we confirmed that the network trained based on point geometric patterns had limited discrimination performance. In this paper, we introduce deep learning based on projected images created from colored 3D point cloud data to improve the accuracy of our structure discrimination method. The projected images are created by a virtual camera system, and after discrimination, predicted pixel-wise labels are back-projected into the 3D point cloud. In evaluation, we reported the discrimination performance and 2D-3D back projection result of our proposed method.