计算机与现代化Issue(1)：44-49,6.DOI:10.3969/j.issn.1006-2475.2025.01.008
位置自适应卷积PointNet++的点云数据分类方法
Point Cloud Data Classification Method of PointNet++with Position Adaptive Convolution
摘要
Abstract
Aiming at the problem of low classification accuracy of point cloud data in complex scenes,a PointNet++deep neural network model based on position adaptive convolution is proposed.Since adaptive position convolution has strong ability to cap-ture fine-grained local features and can fully obtain the spatial variation and geometric structure feature information of three-dimensional point clouds,on the basis of PointNet++network,the proposed method in this paper first obtains the key points through the farthest point sampling,and then uses the K nearest neighbor method to realize grouping according to the key points,and then using the adaptive position convolution replaces the MLP in the original method to extract the local features of each group,and finally completes the point cloud classification.The proposed method was compared on two public point cloud datas-ets S3DIS and Semantic3D.Experimental results show that the overall accuracy and mIoU of the proposed method on the indoor dataset S3DIS are about 2.7 percentage points and 3.2 percentage points higher than PointNet++network,respectively,and the overall accuracy and mIoU of the outdoor dataset Semantic3D are about 2.5 percentage points and 2.1 percentage points higher than PointNet++.关键词
点云分类/位置自适应卷积/PointNet++/深度学习/局部特征Key words
point cloud classification/position adaptive convolution/PointNet++/deep learning/local feature分类
信息技术与安全科学引用本文复制引用
闫晓奇,彭逸清,任小玲..位置自适应卷积PointNet++的点云数据分类方法[J].计算机与现代化,2025,(1):44-49,6.基金项目
陕西省自然科学基础研究计划重点项目(2018JZ6002) （2018JZ6002）
