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A Study on generalization capability of trained structure discrimination network based on 3D point cloud

Imabuchi, Takashi  ; Kawabata, Kuniaki   

This paper describes a feasibility study of a deep learning-based structure discrimination using data sources measured by different sensor from the sensor used when training the network for automatic creation of 3D spatial information in working environment for decommissioning. In previous work, we have developed a method for estimating geometrical shape regions and category information of structures from high-density 3D point cloud data measured in nuclear facilities. In this paper, we report the discrimination accuracy on low-density 3D point cloud data measured in a testing plant environment using the discriminator trained on high-density and high-precision 3D point cloud data measured in the same space. In addition to this, we verify the improvement of accuracy by removing noise on the surface of the structure caused by the sensor's measurement error range.

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