华中科技大学学报（自然科学版）2026，Vol.54Issue(3)：161-167,7.DOI:10.13245/j.hust.250287
融合多尺度上下文的点云语义分割方法
Semantic segmentation method of point cloud based on multi-scale context fusion
摘要
Abstract
Current research on semantic segmentation based on point cloud largely ignores the global context information and lacks the ability to extract point feature.To address the problem,a point cloud semantic segmentation network that combines feature representation augmentation and global context aggregation was proposed.Firstly,it introduces more geometric and feature contexts from explicit 3D space and implicit feature space to the point cloud,and improves the dependency between point descriptors and channel descriptors through bilinear regularization,retaining sufficient global information.Secondly,it combines with dilated convolution to expand the receptive field of the point cloud,enhancing the feature extraction capability.Finally,it fuses multi-scale features of different encoding layers to realize the complementarity of geometric information and semantic feature.Comparative experiments conducted on S3DIS and Semantic3D indicate that mean intersection over union of our proposed method reaches 71.9％and 78.1％respectively,which are 1.9％and 0.7％higher than that of the baseline network RandLA-Net.关键词
语义分割/三维点云/特征表示增强/空洞卷积/多尺度上下文信息Key words
semantic segmentation/3D point cloud/feature representation augmentation/dilated convolution/multi-scale context分类
信息技术与安全科学引用本文复制引用
宋涛,袁川,马婧华,陈挺..融合多尺度上下文的点云语义分割方法[J].华中科技大学学报（自然科学版）,2026,54(3):161-167,7.基金项目
重庆市科技局基础与前沿研究计划资助项目(cstc2021jcyj-msxmX0348) （cstc2021jcyj-msxmX0348）
重庆市自然科学基金创新发展联合基金项目(CSTB2023NSCQ-LZX0068) （CSTB2023NSCQ-LZX0068）
重庆理工大学研究生教育高质量发展行动计划资助成果(gzl-cx20243102). （gzl-cx20243102）
