Full-automatic high-precision scene 3D reconstruction method with water-area intelligent complementation and mesh optimization for UAV images

Fast and high-precision urban scene 3D modeling is the foundational data infrastructure for the digital earth and smart cities. However, due to challenges such as water-area matching difficulties and issues like data redundancy and insufficient observations, existing full-automatic 3D modeling metho...

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Veröffentlicht in:International journal of digital earth Jg. 17; H. 1
Hauptverfasser: Guo, Bingxuan, Ge, Yingwei, Xiao, Xiongwu, Wang, Chao, Gong, Jianya, Li, Deren
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Abingdon Taylor & Francis 31.12.2024
Taylor & Francis Ltd
Taylor & Francis Group
Schlagworte:
Accuracy
base maps
cities
Clustering
Complementation
Culling
data collection
Depth
depth map optimization
digital database
Digital imaging
extracts
High-completeness scene 3D modeling
image analysis
Image processing
Image reconstruction
least squares
mesh refinement
Modelling
Optimization
porous media
Ray tracing
Redundancy
Three dimensional models
UAV images
Water
Water depth
water-area intelligent complementation
ISSN:1753-8947, 1753-8955, 1753-8955
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Zusammenfassung:Fast and high-precision urban scene 3D modeling is the foundational data infrastructure for the digital earth and smart cities. However, due to challenges such as water-area matching difficulties and issues like data redundancy and insufficient observations, existing full-automatic 3D modeling methods often result in water-area missing and many small holes in the models and insufficient local-model accuracy. To overcome these challenges, full-automatic high-precision scene 3D reconstruction method with water-area intelligent complementation on depth maps and mesh optimization is proposed. Firstly, SfM was used to calculated image poses and PatchMatch was used to generated initial depth maps. Secondly, a simplified GAN extracted water-area masks and ray tracing was used achieve high-precision auto-completed water-area depth values. Thirdly, fully connected CRF optimized water-areas and arounds in depth maps. Fourthly, high-precision 3D point clouds were obtained using depth map fusion based on clustering culling and depth least squares. Then, mesh was generated and optimized using similarity measurement and vertex gradients to obtain refined mesh. Finally, high-precision scene 3D models without water-area missing or holes were generated. The results showed that: to compare with the-state-of-art ContextCapture, the proposed method enhances model completeness by 14.3%, raises average accuracy by 14.5% and improves processing efficiency by 63.6%.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1753-8947
1753-8955
1753-8955
DOI:10.1080/17538947.2024.2317441