Research on Point Cloud Registration and Stitching Fusion Algorithm Based on GCN-PRFNet

IF 3.4 3区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS IEEE Access Pub Date : 2025-03-05 DOI:10.1109/ACCESS.2025.3548170
Wenhao Zeng;Gongbing Su;Zixuan Su;Rui Li;Jun Chen
{"title":"Research on Point Cloud Registration and Stitching Fusion Algorithm Based on GCN-PRFNet","authors":"Wenhao Zeng;Gongbing Su;Zixuan Su;Rui Li;Jun Chen","doi":"10.1109/ACCESS.2025.3548170","DOIUrl":null,"url":null,"abstract":"Point-cloud registration and stitching are important topics in the field of robot navigation and 3D reconstruction, e.g., the accuracy of point cloud registration and stitching in robot navigation directly affects the accuracy of map construction. Many researchers have proposed various algorithms for deep learning-based point cloud registration and stitching methods with good performance, and although there are end-to-end methods that have made progress, they still have limitations in local feature fusion efficiency and geometric detail retention. To address this issue, a fusion algorithm for registration and stitching based on a GCN-PRFNet point cloud is proposed. The network has a feature extraction module, a point cloud registration module, and a point cloud splicing and fusion module. GCN-PRFNet can efficiently handle the task of point cloud registration and splicing and fusion in partially overlapping regions and is robust to noise. The model is trained on the ModelNet40 dataset, and its registration and splicing accuracies are improved by 53.9%, 20.1%, 8.3%, 12.2%, 6.1% and 1.8% when compared with the traditional iterative closest point and learning-based PointNetLK, DGCNN, RPM-Net, DCP, and PointViG methods. This indicates that the constructed model is effective in point cloud registration and splicing. Meanwhile, point-cloud registration and splicing tests were performed on five self-constructed artefact datasets, and their registration and splicing accuracies were over 90%, indicating that the constructed end-to-end point-cloud registration and splicing model is considerably effective in real-world application scenarios.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"43384-43397"},"PeriodicalIF":3.4000,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10910120","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Access","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10910120/","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0

Abstract

Point-cloud registration and stitching are important topics in the field of robot navigation and 3D reconstruction, e.g., the accuracy of point cloud registration and stitching in robot navigation directly affects the accuracy of map construction. Many researchers have proposed various algorithms for deep learning-based point cloud registration and stitching methods with good performance, and although there are end-to-end methods that have made progress, they still have limitations in local feature fusion efficiency and geometric detail retention. To address this issue, a fusion algorithm for registration and stitching based on a GCN-PRFNet point cloud is proposed. The network has a feature extraction module, a point cloud registration module, and a point cloud splicing and fusion module. GCN-PRFNet can efficiently handle the task of point cloud registration and splicing and fusion in partially overlapping regions and is robust to noise. The model is trained on the ModelNet40 dataset, and its registration and splicing accuracies are improved by 53.9%, 20.1%, 8.3%, 12.2%, 6.1% and 1.8% when compared with the traditional iterative closest point and learning-based PointNetLK, DGCNN, RPM-Net, DCP, and PointViG methods. This indicates that the constructed model is effective in point cloud registration and splicing. Meanwhile, point-cloud registration and splicing tests were performed on five self-constructed artefact datasets, and their registration and splicing accuracies were over 90%, indicating that the constructed end-to-end point-cloud registration and splicing model is considerably effective in real-world application scenarios.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
求助全文
约1分钟内获得全文 去求助
来源期刊
IEEE Access
IEEE Access COMPUTER SCIENCE, INFORMATION SYSTEMSENGIN-ENGINEERING, ELECTRICAL & ELECTRONIC
CiteScore
9.80
自引率
7.70%
发文量
6673
审稿时长
6 weeks
期刊介绍: IEEE Access® is a multidisciplinary, open access (OA), applications-oriented, all-electronic archival journal that continuously presents the results of original research or development across all of IEEE''s fields of interest. IEEE Access will publish articles that are of high interest to readers, original, technically correct, and clearly presented. Supported by author publication charges (APC), its hallmarks are a rapid peer review and publication process with open access to all readers. Unlike IEEE''s traditional Transactions or Journals, reviews are "binary", in that reviewers will either Accept or Reject an article in the form it is submitted in order to achieve rapid turnaround. Especially encouraged are submissions on: Multidisciplinary topics, or applications-oriented articles and negative results that do not fit within the scope of IEEE''s traditional journals. Practical articles discussing new experiments or measurement techniques, interesting solutions to engineering. Development of new or improved fabrication or manufacturing techniques. Reviews or survey articles of new or evolving fields oriented to assist others in understanding the new area.
期刊最新文献
High-Performance NTRU Accelerator Using a Direct Memory Access Controller Corrections to “A Visual Prompt-Based Mobile Learning System for Improved Algebraic Understanding in Students With Learning Disabilities” Utility Meets Privacy: A Critical Evaluation of Tabular Data Synthesizers Minimizing Delay at Closely Spaced Signalized Intersections Through Green Time Ratio Optimization: A Hybrid Approach With K-Means Clustering and Genetic Algorithms Analysis and Design of mmWave Passive CMOS Circulators With Unequal Port Impedances
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1