{"title":"Multi-view stereo 3D building reconstruction with sparse depth and edge location priors","authors":"Xuan Yang , Rongrong Hou , Yuequan Bao","doi":"10.1016/j.autcon.2025.106365","DOIUrl":null,"url":null,"abstract":"<div><div>Accurate 3D building reconstruction remains challenging for large-scale structures with complex geometry. While deep learning-based Multi-View Stereo (MVS) methods improve upon traditional approaches, they exhibit errors in depth-discontinuous regions due to insufficient depth priors and architectural feature integration. To address these issues, this paper introduces ISENet, featuring: (1) an adaptive feature fusion framework for enhanced UAV image feature extraction, and (2) a multi-stage edge-aware depth hypothesis module leveraging sparse depth and edge location priors. Evaluations demonstrate state-of-the-art performance on the DTU dataset with Accuracy (0.238 mm) and Overall (0.277 mm) metrics. On real-world data, ISENet achieves 14.4 mm modeling error and 235 times higher point cloud density than commercial solutions. The approach also generalizes to standard MVS scenarios.</div></div>","PeriodicalId":8660,"journal":{"name":"Automation in Construction","volume":"177 ","pages":"Article 106365"},"PeriodicalIF":9.6000,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Automation in Construction","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0926580525004054","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CONSTRUCTION & BUILDING TECHNOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Accurate 3D building reconstruction remains challenging for large-scale structures with complex geometry. While deep learning-based Multi-View Stereo (MVS) methods improve upon traditional approaches, they exhibit errors in depth-discontinuous regions due to insufficient depth priors and architectural feature integration. To address these issues, this paper introduces ISENet, featuring: (1) an adaptive feature fusion framework for enhanced UAV image feature extraction, and (2) a multi-stage edge-aware depth hypothesis module leveraging sparse depth and edge location priors. Evaluations demonstrate state-of-the-art performance on the DTU dataset with Accuracy (0.238 mm) and Overall (0.277 mm) metrics. On real-world data, ISENet achieves 14.4 mm modeling error and 235 times higher point cloud density than commercial solutions. The approach also generalizes to standard MVS scenarios.
期刊介绍:
Automation in Construction is an international journal that focuses on publishing original research papers related to the use of Information Technologies in various aspects of the construction industry. The journal covers topics such as design, engineering, construction technologies, and the maintenance and management of constructed facilities.
The scope of Automation in Construction is extensive and covers all stages of the construction life cycle. This includes initial planning and design, construction of the facility, operation and maintenance, as well as the eventual dismantling and recycling of buildings and engineering structures.