Point Cloud Semantic Segmentation Network Based on Feature Fusion and Loss Optimization
Aiming at the problem that most of the current methods only use single-scale features but ignore the multi-scale feature information with different receptive fields and cannot effectively deal with unbalanced category weights in point cloud datasets,a segmentation network(FFBL-Net)based on full-stage feature fusion(FSFF)and balanced loss(BL)is proposed.First,FSFF module promotes the complementation of shallow and deep semantic information by integrating learnable features of different coding stages with features of the current stage.The fused features are transferred to the encoding fusion module(EFM)and decoding fusion module(DFM),which realizes the cross-stage fusion of features.In addition,to solve the problem of unbalanced class distribution in the dataset,BL loss is introduced to adjust the gradient difference between categories.The experimental results show that the FFBL-Net on the large-scale point cloud dataset S3DIS has reached69.7%in terms of mean intersection over union(mIoU)and 89.9%in overall accuracy(OA),which is12.4%and 6.1%higher than that of the original PointNet++ respectively.
point cloudsemantic segmentationmulti-level feature fusionloss optimizationneural network optimization
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