Instance Segmentation Network Based on IW Method of Self-attention Mechanism and 3D-BoNet Algorithm
Aiming at the difficulty of point cloud feature extraction and low robustness in instance segmen-tation algorithms,an instance segmentation network(IW-BoNet)based on self-attention mechanism and 3D-BoNet algorithm was proposed.In the stage of feature extraction,a novel approach leveraging the self-attention mechanism,named of Instance Wise(IW),was proposed.The utilization of a self-attention module enabled effective learning of feature weights and facilitates capturing comprehensive contextual in-formation pertaining to each instance.The Euclidean distance loss function in the 3D-BoNet model was re-placed with the Smooth L1 loss function.The performance test on the STPLS3D dataset shows that com-pared with the original 3D-BoNet model,the average mean accuracy of IW-BoNet model is improved by 6.2％,and the robustness is improved,which can extract the instance information more efficiently.
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