Scene information guided aerial photogrammetric mission recomposition towards detailed level building reconstruction

IF 8 1区 工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Akram Akbar , Chun Liu , Hangbin Wu , Shoujun Jia , Zeran Xu
{"title":"Scene information guided aerial photogrammetric mission recomposition towards detailed level building reconstruction","authors":"Akram Akbar ,&nbsp;Chun Liu ,&nbsp;Hangbin Wu ,&nbsp;Shoujun Jia ,&nbsp;Zeran Xu","doi":"10.1016/j.aei.2024.102913","DOIUrl":null,"url":null,"abstract":"<div><div>Real 3D building models have become indispensable data sources for building spatial information bases for smart cities by leveraging structural correlations and rich semantic expressions of real-world scene entities. The essential prerequisite for real 3D reconstruction is real-time and dynamic detailed-level observations. Low-altitude multicopter UAV platforms are optimal for automatic and periodic building scene observations. However, there are still several challenges in UAV-based path planning for real 3D data capture while maintaining the overall fidelity of architectural details due to observational scale variations, surrounding uncertainties, structural complexity, and topological delicacy. We propose a scene information guided aerial photogrammetric mission recomposition method in response to this challenge. Depending on the architectural complexity, the two proposed observation patterns, parallel inspection and surface enveloping, can be recomposed to achieve UAV obstacle avoidance and complete coverage of individual buildings in a restricted space, capturing global surface detail with millimeter resolution and low texture distortion. The virtual simulation environment, which is constructed based on the semantics and elevation values of the surroundings, provides a basis for selecting the observation pattern and optimal flight parameters based on the reconstruction requirements of the building. In order to achieve quality control of 3D reconstruction models, this paper introduces a reconstruction quality assessment scheme consisting of four quantitative evaluation metrics, namely coverage, resolution distribution, texture distortion score, and geometric accuracy, which effectively establishes a close relationship between mission planning and 3D reconstruction. The observation capability of the proposed method is better than other typical observation patterns, obtaining a model of globally homogeneous resolution distribution over the main body of the building, reaching an average level of 7.01 mm and the highest level of 2.12 mm (façade region), which can provide high-quality data for the semantic extraction and instantiation of multiple surface elements of buildings.</div></div>","PeriodicalId":50941,"journal":{"name":"Advanced Engineering Informatics","volume":"62 ","pages":"Article 102913"},"PeriodicalIF":8.0000,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advanced Engineering Informatics","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1474034624005640","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

Abstract

Real 3D building models have become indispensable data sources for building spatial information bases for smart cities by leveraging structural correlations and rich semantic expressions of real-world scene entities. The essential prerequisite for real 3D reconstruction is real-time and dynamic detailed-level observations. Low-altitude multicopter UAV platforms are optimal for automatic and periodic building scene observations. However, there are still several challenges in UAV-based path planning for real 3D data capture while maintaining the overall fidelity of architectural details due to observational scale variations, surrounding uncertainties, structural complexity, and topological delicacy. We propose a scene information guided aerial photogrammetric mission recomposition method in response to this challenge. Depending on the architectural complexity, the two proposed observation patterns, parallel inspection and surface enveloping, can be recomposed to achieve UAV obstacle avoidance and complete coverage of individual buildings in a restricted space, capturing global surface detail with millimeter resolution and low texture distortion. The virtual simulation environment, which is constructed based on the semantics and elevation values of the surroundings, provides a basis for selecting the observation pattern and optimal flight parameters based on the reconstruction requirements of the building. In order to achieve quality control of 3D reconstruction models, this paper introduces a reconstruction quality assessment scheme consisting of four quantitative evaluation metrics, namely coverage, resolution distribution, texture distortion score, and geometric accuracy, which effectively establishes a close relationship between mission planning and 3D reconstruction. The observation capability of the proposed method is better than other typical observation patterns, obtaining a model of globally homogeneous resolution distribution over the main body of the building, reaching an average level of 7.01 mm and the highest level of 2.12 mm (façade region), which can provide high-quality data for the semantic extraction and instantiation of multiple surface elements of buildings.
场景信息指导下的航空摄影测量任务重构,实现详细级别的建筑物重建
利用真实世界场景实体的结构关联和丰富的语义表达,真实三维建筑模型已成为建立智慧城市空间信息库不可或缺的数据源。真实三维重建的基本前提是实时和动态的细节级观测。低空多旋翼无人机平台是自动定期观测建筑场景的最佳选择。然而,由于观测尺度的变化、周围环境的不确定性、结构的复杂性和拓扑的微妙性,在基于无人机的路径规划以获取真实三维数据的同时保持建筑细节的整体保真度方面仍面临着一些挑战。针对这一挑战,我们提出了一种场景信息指导下的航空摄影测量任务重组方法。根据建筑的复杂程度,我们提出的两种观测模式--平行检测和表面包络--可以重新组合,以实现无人机避障和在有限空间内完整覆盖单个建筑,以毫米级分辨率和低纹理失真捕捉全局表面细节。根据周围环境的语义和高程值构建的虚拟仿真环境,为根据建筑物的重建要求选择观测模式和最佳飞行参数提供了依据。为了实现三维重建模型的质量控制,本文介绍了一种由覆盖率、分辨率分布、纹理失真评分和几何精度四个定量评价指标组成的重建质量评估方案,有效地建立了任务规划与三维重建之间的密切关系。该方法的观测能力优于其他典型观测模式,可获得建筑物主体全局均匀分辨率分布的模型,平均分辨率达到 7.01 mm,最高分辨率达到 2.12 mm(立面区域),可为建筑物多表面元素的语义提取和实例化提供高质量数据。
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
Advanced Engineering Informatics
Advanced Engineering Informatics 工程技术-工程:综合
CiteScore
12.40
自引率
18.20%
发文量
292
审稿时长
45 days
期刊介绍: Advanced Engineering Informatics is an international Journal that solicits research papers with an emphasis on 'knowledge' and 'engineering applications'. The Journal seeks original papers that report progress in applying methods of engineering informatics. These papers should have engineering relevance and help provide a scientific base for more reliable, spontaneous, and creative engineering decision-making. Additionally, papers should demonstrate the science of supporting knowledge-intensive engineering tasks and validate the generality, power, and scalability of new methods through rigorous evaluation, preferably both qualitatively and quantitatively. Abstracting and indexing for Advanced Engineering Informatics include Science Citation Index Expanded, Scopus and INSPEC.
×
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