Objectives The point cloud data of hull segment closure obtained by 3D scanner has the advantages of high precision and large data volume, and can reflect the construction status of segment closure well. Since the existing Pointnet++ network is unable to process large-capacity point cloud data, an intelligent recognition algorithm for hull segment closure surface components based on improved Pointnet++ is proposed to realize intelligent recognition of components for large-capacity hull segment convergence surface point cloud data.
Methods Based on hypervoxel growth theory, hull segment closure point cloud data is segmtioned and simplified, and hull segment closure point cloud data set is constructed, and the data set is used to train the Pointnet++ network improved based on deep learning theory.
Results The convergence results of the network model on the training and testing sets of point cloud data for hull segment closure surfaces tend to be stable, achieving an accuracy rate of 90% on the testing set.
Conclusions This indicates that the method has good recognition ability and can achieve intelligent recognition of hull segment closure surface components.
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