DOI number:10.1080/17538947.2024.2317441

Affiliation of Author(s):LIESMARS, Wuhan University

Teaching and Research Group:航空航天摄影测量研究室

Journal:International Journal of Digital Earth

Funded by:国家自然科学基金、湖北省自然科学基金

Abstract: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%.

Co-author:Yingwei Ge,Chao Wang,Jianya Gong,Deren Li

Indexed by:Journal paper

Correspondence Author:Xiongwu Xiao

Discipline:Engineering

Document Type:J

Volume:17

Issue:1

Page Number:2317441

Number of Words:12500

Translation or Not:no

Date of Publication:2024-02-16

Included Journals:SCI

Links to published journals:https://doi.org/10.1080/17538947.2024.2317441