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Discrimination of Plant Structures in 3D Point Cloud Through Back-Projection of Labels Derived from 2D Semantic Segmentation

Imabuchi, Takashi  ; Kawabata, Kuniaki   

In the decommissioning of Fukushima Daiichi Nuclear Power Station, radiation dose calculations using a 3D model of the workspace are performed to determine appropriate measures to reduce exposure. However, constructing a 3D model from 3D point cloud is costly. In order to separate the geometrical shape regions on 3D point cloud, we have been developing the structure discrimination method by 3D and 2D deep learning for contributing to 3D modeling automation technology. In this paper, we describe a method for transferring and fusing labels to handle 2D prediction label in 3D space. We propose an exhaustive label fusion method for plant facilities with intricate structures. In evaluation, we applied the method to a mock-up plant dataset and confirmed that it works effectively.

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