The multibeam sonar has become a powerful tool for undersea pipeline detection, which can obtain high quality three-dimensional point cloud data. However, the identification accuracy of current pipeline algorithms remains low because of insufficient utilization of context features. Therefore, this paper proposes an improved context-learning point cloud segmentation network, named Cross-scale Point-In-Context (Cross-PIC), to identify submarine pipelines. Cross-PIC designs the sampling module Balanced-FPS to improve the equalization sampling. Meanwhile, Cross-PIC constructs different scale coding architecture based on context learning, which includes Auxiliary line and Baseline for multi-scale feature encoding. Finally, the Refine Module is designed to realize cross-scale coded feature fusion to output segmentation results. Experiments with different fuzzy degrees show that Cross-PIC also significantly outperforms the original Point-In-Context network with less training data. Cross-PIC also achieves better results than other networks based on point, voxel, and dynamic graph convolution.
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