计算机技术与发展2025，Vol.35Issue(3)：49-55,7.DOI:10.20165/j.cnki.ISSN1673-629X.2024.0340
用于点云语义分割的局部特征增强网络
Local Feature Enhancement Network for Point Cloud Semantic Segmentation
摘要
Abstract
Compared to two-dimensional image data,3D point cloud data generally has disorder,sparsity,and uneven density.At the same time,the correlation between points and the acquisition of structural information are also great challenges.It is precisely because of these characteristics of point clouds that semantic segmentation of objects with insignificant feature information has always been a major challenge in the field of point cloud processing.To this end,a local feature enhancement network for point cloud semantic segmentation is proposed.The algorithm designs a local feature aggregation module to enhance feature information.This module concatenates the relative point positions of the point cloud with its corresponding point features to obtain an enhanced feature vector,achieving the enhancement of feature information.Through this module,the network can effectively learn complex local structural information and enhance its ability to process local geometric information.In addition,the proposed algorithm uses an inverse density function to assign larger weights to points in sparse regions and smaller weights to points in dense regions when extracting features,effectively eliminating the influence of uneven density of point clouds.The experimental results show that the proposed algorithm achieves an average intersection-union ratio of 67.4％and an overall accuracy of 88.4％on the Stanford large-scale 3D indoor spatial dataset,which is 11.3％ and 4.3％ higher than DGCNN respectively.It significantly improves the segmentation effect of objects with insignificant feature information.关键词
图像处理/点云语义分割/局部特征增强/反密度函数/深度学习/特征向量Key words
image processing/point cloud semantic segmentation/local feature enhancement/inverse density function/deep learning/feature vector分类
信息技术与安全科学引用本文复制引用
柴玉晶,梁坤豪,杨历省,宫卫光..用于点云语义分割的局部特征增强网络[J].计算机技术与发展,2025,35(3):49-55,7.基金项目
山东省自然科学基金(ZR2023QA064) （ZR2023QA064）
枣庄学院博士科研基金项目(1020733) （1020733）
