计算机技术与发展2024，Vol.34Issue(5)：66-72,7.DOI:10.20165/j.cnki.ISSN1673-629X.2024.0042
基于特征融合和损失优化的点云语义分割网络
Point Cloud Semantic Segmentation Network Based on Feature Fusion and Loss Optimization
摘要
Abstract
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.关键词
点云/语义分割/多尺度特征融合/损失优化/神经网络优化Key words
point cloud/semantic segmentation/multi-level feature fusion/loss optimization/neural network optimization分类
信息技术与安全科学引用本文复制引用
刘起源,路锦正,黄炳森..基于特征融合和损失优化的点云语义分割网络[J].计算机技术与发展,2024,34(5):66-72,7.基金项目
国家重点研发计划项目(2019YFB1705100) （2019YFB1705100）
四川省科技计划项目(2022ZHCG0001) （2022ZHCG0001）
黑龙江省重点研发计划项目(2022ZX01A16) （2022ZX01A16）
