计算机工程与应用2023，Vol.59Issue(24)：209-215,7.DOI:10.3778/j.issn.1002-8331.2209-0004
基于3D特征动态融合的点云特征提取网络
Point Cloud Feature Extraction Network Based on 3D Feature Dynamic Fusion
摘要
Abstract
To solve the problem that the feature extraction methods currently used for point cloud registration do not ade-quately extract effective information from point cloud,a point cloud feature extraction network DFRUNet(3D feature dynamic fusion and residual u-net)based on 3D feature dynamic fusion is proposed.The network dynamically fuses the features of encoding and decoding modules through 3DFDF(3D feature dynamic fusion)module to extract sufficient information from the point cloud.Meanwhile,the SE-Res(squeeze and excitation residual)module is used to extract point cloud features.By dynamically adjusting the weights of significant areas,the area features are extracted to improve the quality of the extracted features.Secondly,map the features extracted from the network into high-dimensional space,and complete point cloud registration using RANSAC(random sample consensus)algorithm.The experimental results show that on the 3DMatch dataset,the FMR(feature-match recall)of the algorithm is 96.3%,which is 0.011 higher than that of the classical FCGF algorithm.The registration recall rate is 82.2%and increased by 0.014.This method fully extracts the effective information from point clouds and achieves a higher recall rate,which has reference value for other point cloud registration studies.关键词
特征提取/点云配准/特征动态融合/深度学习Key words
feature extraction/point cloud registration/feature dynamic fusion/deep learning分类
计算机与自动化引用本文复制引用
孙刘杰,翟仁杰,王文举,庞茂然..基于3D特征动态融合的点云特征提取网络[J].计算机工程与应用,2023,59(24):209-215,7.基金项目
上海市科学技术委员会科研计划项目(18060502500) （18060502500）
上海市自然科学基金面上项目(19ZR1435900). （19ZR1435900）
