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发表时间：2019-01-01

发表刊物：Ship Engineering

卷号：41

期号：12

页面范围：138-141

ISSN号：1000-6982

关键字："PointNet++; 3D scanner; point cloud; PointNet++; block erection surface; deep learning"

CN号：31-1281/U

摘要：The accuracy detection of block erection surface is an important part of assembling and erection process. In terms of the accuracy detection of block erection surface, the 3D scanner has a huge advantage over the total station. However, the 3D scanner records many points that are not related to block erection surface during the scanning process. Therefore, the block erection surface is intelligently recognized by point cloud data scanned by 3D scanner. Appropriate improvements have been made to the PointNet++ network according to the deep learning theory. The point cloud data derived from the CAD model is used to construct the labeled point cloud data set, and then the Adam algorithm is used to optimize the network. Finally, the network's recognition of block erection surface achieves 73% precision and 90% recall on validation data set.

备注：新增回溯数据