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Kato, Toru*; Takahashi, Hiroki*; Yamashita, Meguru*; Doi, Akio*; Imabuchi, Takashi  

In this study, we used the deep learning models PointNet++ and PointNeXt to automatically recognize structures such as pipes and ducts from large-scale residential point cloud data measured by laser scanners such as Faro, and compared the accuracy of each method. The point cloud data corresponds to multiple types of CAD data, and a model was created by labeling the point cloud that was closest to the ID of each CAD data, and this was used as training data. Using this learning model, structures were recognized from point cloud data of differences that change over time, and the accuracy was evaluated.

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