广西师范大学学报（自然科学版）2025，Vol.43Issue(4)：1-14,14.DOI:10.16088/j.issn.1001-6600.2024060301
基于改进PointNet++的城市道路点云分类方法
Point Cloud Classification Method of Urban Roads Based on Improved PointNet++
摘要
Abstract
The large amount of point cloud data,unbalanced distribution and uneven density of urban road scenes make it difficult for the current point cloud classification methods to meet the requirements of high-precision classification.To deal with the problem of insufficient local feature extraction by PointNet++networks,a local feature aggregation module is designed based on the attention mechanism,which adequately captures local information by dynamically merging neighboring point features.Considering that the existing classification models cannot take into account contextual information,which leads to limited classification performance in complex scenes,a dual-attention module and a context-aware module are constructed to extract contextual information from several dimensions to further enhance the feature representation capability.The experimental results show that the new method has higher accuracy and stronger generalization performance(overall accuracy reaches 98.70%and 96.84%in Oakland and Paris publicly available datasets)under large point cloud datasets,and is more suitable for large-scale point cloud classification.关键词
点云分类/PointNet++/局部特征/注意力机制/上下文信息/城市道路Key words
point cloud classification/PointNet++/local feature/attention mechanism/contextual information分类
交通工程引用本文复制引用
田晟,熊辰崟,龙安洋..基于改进PointNet++的城市道路点云分类方法[J].广西师范大学学报（自然科学版）,2025,43(4):1-14,14.基金项目
广东省自然科学基金(2020A1515010382,2021A1515011587) （2020A1515010382,2021A1515011587）
