河南城建学院学报2026，Vol.35Issue(4)：90-99,10.DOI:10.14140/j.cnki.hncjxb.2026.04.010
基于原型引导损失的双偏移局部特征融合的点云语义分割网络
A prototype-guided dual-offset local feature aggregation network for point cloud semantic segmentation
摘要
Abstract
PointNet++is limited in extracting local features and the cross-entropy loss lacks regularization capability.To address these issues,a prototype-guided dual-offset local feature fusion network(DOGA-Net)is proposed.The network consists of Dual-Offset Guided Aggregation module and Prototype-Guided Loss module.Dual-Offset Guided Aggregation module utilizes a lightweight attention mechanism based on dual offset to generate attention weights that are both direction sensitive and structural dependent,enabling dis-criminative aggregation of neighborhood features and enhancing the representation of local geometric struc-tures.Prototype-Guided Loss module constructs feature prototypes for each semantic category and enforces point feature to cluster around their corresponding prototypes in the feature space to improve intra-class con-sistency.Experiments on the ISPRS dataset,LASDU dataset and GML dataset are conducted to validate the performance of DOGA-Net.The results show that,compared to the benchmark network PointNet++,DO-GA-Net achieves improvements in mF1 and overall accuracy(OA)with OA reaching 83.8%,87.7%and 95.8%,and mF1 reaching 72.3%,78.0%and 70.4%on the three datasets,respectively,fully demonstrat-ing its effectiveness.关键词
点云数据/语义分割/PointNet++/局部注意力Key words
point cloud/semantic segmentation/PointNet++/local attention分类
信息技术与安全科学引用本文复制引用
张吉..基于原型引导损失的双偏移局部特征融合的点云语义分割网络[J].河南城建学院学报,2026,35(4):90-99,10.基金项目
国家自然科学基金项目(42374147) （42374147）
