A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Computer vision-based construction progress monitoring
Abstract Automating the process of construction progress monitoring through computer vision can enable effective control of projects. Systematic classification of available methods and technologies is necessary to structure this complex, multi-stage process. Using the PRISMA framework, relevant studies in the area were identified. The various concepts, tools, technologies, and algorithms reported by these studies were iteratively categorised, developing an integrated process framework for Computer-Vision-Based Construction Progress Monitoring (CV-CPM). This framework comprises: data acquisition and 3D-reconstruction, as-built modelling, and progress assessment. Each stage is discussed in detail, positioning key studies, and concurrently comparing the methods used therein. The four levels of progress monitoring are defined and found to strongly influence all stages of the framework. The need for benchmarking CV-CPM pipelines and components are discussed, and potential research questions within each stage are identified. The relevance of CV-CPM to support emerging areas such as Digital Twin is also discussed.
Graphical abstract Display Omitted
Highlights A comprehensive review and categorisation of recent studies on computer vision-based construction progress monitoring. An integrated framework for Computer Vision-Based Construction Progress Monitoring (CV-CPM). Establishing a need for exploring a hybrid approach (heuristic-learning based) for as-built modelling. Categorisation of on-site progress monitoring requirements into four levels and associating these with the CV-CPM framework. Discussion on usage of the proposed framework for developing a strategy and roadmap to enable benchmarking in CV-CPM.
Computer vision-based construction progress monitoring
Abstract Automating the process of construction progress monitoring through computer vision can enable effective control of projects. Systematic classification of available methods and technologies is necessary to structure this complex, multi-stage process. Using the PRISMA framework, relevant studies in the area were identified. The various concepts, tools, technologies, and algorithms reported by these studies were iteratively categorised, developing an integrated process framework for Computer-Vision-Based Construction Progress Monitoring (CV-CPM). This framework comprises: data acquisition and 3D-reconstruction, as-built modelling, and progress assessment. Each stage is discussed in detail, positioning key studies, and concurrently comparing the methods used therein. The four levels of progress monitoring are defined and found to strongly influence all stages of the framework. The need for benchmarking CV-CPM pipelines and components are discussed, and potential research questions within each stage are identified. The relevance of CV-CPM to support emerging areas such as Digital Twin is also discussed.
Graphical abstract Display Omitted
Highlights A comprehensive review and categorisation of recent studies on computer vision-based construction progress monitoring. An integrated framework for Computer Vision-Based Construction Progress Monitoring (CV-CPM). Establishing a need for exploring a hybrid approach (heuristic-learning based) for as-built modelling. Categorisation of on-site progress monitoring requirements into four levels and associating these with the CV-CPM framework. Discussion on usage of the proposed framework for developing a strategy and roadmap to enable benchmarking in CV-CPM.
Computer vision-based construction progress monitoring
Reja, Varun Kumar (author) / Varghese, Koshy (author) / Ha, Quang Phuc (author)
2022-03-31
Article (Journal)
Electronic Resource
English
Progress monitoring , Computer vision , Automated construction , Data acquisition , 3D reconstruction , As-built modelling , Point cloud , Scan to BIM , Literature review , Digital Twin , AEC , Architecture, Engineering and Construction , AI , Artificial Intelligence , AR , Augmented Reality , BIM , Building Information Modelling , BoQ , Bill of Quantities , CAD , Computer-Aided Design , CCTV , Closed-Circuit Television , CMVS , Clustering Multi-View Stereo , CNN , Convolutional Neural Network , CV-CPM , Computer Vision-based Construction Progress Monitoring , DT , DL , Deep Learning , GPS , Global Positioning System , GPU , Graphical Processing Unit , GUI , Graphical User Interface , HSI , Hue-Saturation-Intensity , ICP , Iterative Closest Point , IFC , Industry Foundation Class , IMU , Inertial Measurement Unit , IoT , Internet of Things , LiDAR , Light Detection and Ranging , LoD , Level of Detail , LPM , Level of Progress Monitoring , MEP , Mechanical, Electrical, and Plumbing , ML , Machine Learning , MR , Mixed Reality , MVS , Multi-View Stereo , PCA , Principal Component Analysis , PMVS , Patch-based Multi-View Stereo , PSR , Poisson's Surface Reconstruction , QR , Quick Response , RANSAC , Random Sample Consensus , RFID , Radio Frequency Identification , RGBD , Red Green Blue Depth , SfM , Structure-from-Motion , SGM , Semi-Global Matching , SLAM , Simultaneous Localisation and Mapping , SMS , Short Message Service , TLS , Terrestrial Laser Scanning , UAV , Unmanned Aerial Vehicle , UGV , Unmanned Ground Vehicle , VR , Virtual Reality , XR , Extended Reality
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