摘要
近些年,越来越多有关深度学习的方法应用到了点云分类领域。文章提出一种基于多尺度图卷积的点云分类网络,通过获取不同范围内等间隔采样的k个邻近点,以搭建不同尺度的图卷积模块来学习多尺度局部特征。实验表明,该网络在ModelNet40数据集上的准确率达到了92.8%,具有较好的性能。
In recent years,more and more depth learning about the method is applied to the point cloud classification fields.This paper presents a point cloud classification network based on multi-scale graph convolution,and by obtaining the k neighboring points equidistant sampling in the different ranges,establishes graph convolution module of different scales learn multi-scale local features.The experiment shows that the accuracy of the network on the data set ModelNet40 reached 92.8%,with a good performance.
出处
《科技创新与应用》
2021年第8期99-101,共3页
Technology Innovation and Application
基金
湖北省教育厅科学技术研究项目(编号:Q20201409)。
关键词
图卷积
点云分类
多尺度
局部特征
graph convolution
point cloud classification
multi-scale
local features