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今渕 貴志; 羽成 敏秀; 川端 邦明
Proceedings of 2025 IEEE/SICE International Symposium on System Integration (SII2025), p.1416 - 1421, 2025/01
This paper describes a 3D reconstruction based on grouping similar structures for the aim of generating 3D information for understanding the workspace from the images acquired inside the Primary Containment Vessel (PCV) of the Fukushima Daiichi Nuclear Power Station. In the decommissioning works, preliminary surveys are carried out in the PCV, and the workers need to understand the workspace from a large amount of camera images, which requires a great deal of effort. We are currently working on 3D reconstruction from camera images of the PCV, however, one of the challenges is to improve the visibility of reconstructed model containing noise and artifact. In this study, we propose a method of grouping similar structures on the image and utilizing predicted group labels for 3D reconstruction process to highlight structures shapes and to refine 3D modeling. Our key idea is to perform unsupervised segmentation for grouping similar structures that are suitable for images acquired in the PCV because they are difficult to assign correct semantics for unclear structures and the few learning resources. We show on the reasonable performance of our method by validating it by video images of a typical plant environment and survey videos of the PCV taken under adverse conditions such as radiation noise.
川端 邦明; 八代 大*; 羽成 敏秀; 今渕 貴志
no journal, ,
In this report, we report the demonstration test of 3D modeling from image sequences by integrating the efficient modeling methods we have developed. The pipeline process consists of removing similar images and merging between models obtained from partial image sequences. Experimental results by combined method are described.
羽成 敏秀; 今渕 貴志; 川端 邦明
no journal, ,
福島第一原子力発電所の廃炉作業におけるオペレータの空間認識を支援するための立体復元手法を開発している。本稿では、画像選択アルゴリズムを改良し、GPUコンピューティングを適用して立体復元計算を高速化することを試みた。適用結果は、画像選択手法が立体復元計算の高速化に寄与できることを示唆している。