3D point cloud reconstruction using panoramic images

IF 2.3 Q2 REMOTE SENSING
Surendra Kumar Sharma, Kamal Jain, Anoop Kumar Shukla
{"title":"3D point cloud reconstruction using panoramic images","authors":"Surendra Kumar Sharma,&nbsp;Kamal Jain,&nbsp;Anoop Kumar Shukla","doi":"10.1007/s12518-024-00563-w","DOIUrl":null,"url":null,"abstract":"<div><p>Panorama photogrammetry, the process of analyzing panoramic images, has gained popularity in close-range photogrammetry for 3D reconstruction over the past decade. Initially, researchers utilized cylindrical or spherical panoramic images created through specialized cameras or conventional ones with rectilinear lenses. However, these methods were hindered by the high cost of panorama equipment and the need for manual reconstruction. Consequently, there's a growing demand for automated algorithms capable of reconstructing 3D point clouds from stitched panorama images. This study aims to provide a cost-effective solution for automatic 3D point cloud reconstruction from panoramas. The study is divided into two parts; it first outlines an image acquisition strategy for capturing overlapping perspective images to facilitate accurate panorama generation. Subsequently, it introduces an automated algorithm for 3D point cloud reconstruction from panorama images. The process utilizes the KAZE feature detector for feature detection and introduces a novel feature matching approach for matching panorama images. Accuracy assessment of the reconstructed 3D point clouds was done using three methods: Line Segment Based approach, yielding RMSE errors of 34.2mm and 39mm for dataset-1 and dataset-2 respectively, No-Reference 3D Point Cloud Quality Assessment, resulting in quality scores of 8.5939 and 7.4535 for dataset-1 and dataset-2 respectively, and M3C2 distance method computed value of 0.091059 and 0.165179 respectively, indicating high quality of the generated point clouds.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":null,"pages":null},"PeriodicalIF":2.3000,"publicationDate":"2024-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s12518-024-00563-w.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Geomatics","FirstCategoryId":"1085","ListUrlMain":"https://link.springer.com/article/10.1007/s12518-024-00563-w","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"REMOTE SENSING","Score":null,"Total":0}
引用次数: 0

Abstract

Panorama photogrammetry, the process of analyzing panoramic images, has gained popularity in close-range photogrammetry for 3D reconstruction over the past decade. Initially, researchers utilized cylindrical or spherical panoramic images created through specialized cameras or conventional ones with rectilinear lenses. However, these methods were hindered by the high cost of panorama equipment and the need for manual reconstruction. Consequently, there's a growing demand for automated algorithms capable of reconstructing 3D point clouds from stitched panorama images. This study aims to provide a cost-effective solution for automatic 3D point cloud reconstruction from panoramas. The study is divided into two parts; it first outlines an image acquisition strategy for capturing overlapping perspective images to facilitate accurate panorama generation. Subsequently, it introduces an automated algorithm for 3D point cloud reconstruction from panorama images. The process utilizes the KAZE feature detector for feature detection and introduces a novel feature matching approach for matching panorama images. Accuracy assessment of the reconstructed 3D point clouds was done using three methods: Line Segment Based approach, yielding RMSE errors of 34.2mm and 39mm for dataset-1 and dataset-2 respectively, No-Reference 3D Point Cloud Quality Assessment, resulting in quality scores of 8.5939 and 7.4535 for dataset-1 and dataset-2 respectively, and M3C2 distance method computed value of 0.091059 and 0.165179 respectively, indicating high quality of the generated point clouds.

利用全景图像重建 3D 点云
全景摄影测量是对全景图像进行分析的过程,在过去十年中,它在用于三维重建的近距离摄影测量中越来越受欢迎。最初,研究人员利用专用相机或带有直角镜头的传统相机制作圆柱形或球形全景图像。然而,这些方法因全景设备成本高昂和需要手动重建而受到阻碍。因此,对能够从拼接的全景图像中重建三维点云的自动算法的需求日益增长。本研究旨在为从全景图像自动重建三维点云提供一种经济有效的解决方案。研究分为两个部分:首先概述了一种图像采集策略,用于捕捉重叠的透视图像,以便准确生成全景图。随后,它介绍了一种从全景图像重建三维点云的自动算法。该过程利用 KAZE 特征检测器进行特征检测,并引入一种新颖的特征匹配方法来匹配全景图像。使用三种方法对重建的三维点云进行了精度评估:基于线段的方法,数据集-1 和数据集-2 的 RMSE 误差分别为 34.2 毫米和 39 毫米;无参照三维点云质量评估,数据集-1 和数据集-2 的质量得分分别为 8.5939 和 7.4535;M3C2 距离法计算值分别为 0.091059 和 0.165179,表明生成的点云质量很高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Applied Geomatics
Applied Geomatics REMOTE SENSING-
CiteScore
5.40
自引率
3.70%
发文量
61
期刊介绍: Applied Geomatics (AGMJ) is the official journal of SIFET the Italian Society of Photogrammetry and Topography and covers all aspects and information on scientific and technical advances in the geomatics sciences. The Journal publishes innovative contributions in geomatics applications ranging from the integration of instruments, methodologies and technologies and their use in the environmental sciences, engineering and other natural sciences. The areas of interest include many research fields such as: remote sensing, close range and videometric photogrammetry, image analysis, digital mapping, land and geographic information systems, geographic information science, integrated geodesy, spatial data analysis, heritage recording; network adjustment and numerical processes. Furthermore, Applied Geomatics is open to articles from all areas of deformation measurements and analysis, structural engineering, mechanical engineering and all trends in earth and planetary survey science and space technology. The Journal also contains notices of conferences and international workshops, industry news, and information on new products. It provides a useful forum for professional and academic scientists involved in geomatics science and technology. Information on Open Research Funding and Support may be found here: https://www.springernature.com/gp/open-research/institutional-agreements
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信