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Automatic Geometric Modeling of Box Girder Bridge Based on Laser Scanned Point Cloud

https://doi.org/10.2139/ssrn.4784125
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Automated bridge component recognition from point clouds using deep learning
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Proceedings of the 18th International Conference on Computing in Civil and Building Engineering
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A review of 3D reconstruction techniques in civil engineering and their applications
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Applications of 3D point cloud data in the construction industry: A fifteen-year review from 2004 to 2018
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Rapid, on-site spatial information acquisition and its use for infrastructure operation and maintenance
inferredcites →https://doi.org/10.1016/j.autcon.2012.06.003openalex-extracted
Evaluation of accuracy of as-built 3D modeling from photos taken by handheld digital cameras
inferredcites →https://doi.org/10.1016/j.autcon.2012.10.006openalex-extracted
Automatic creation of semantically rich 3D building models from laser scanner data
inferredcites →https://doi.org/10.1016/j.autcon.2014.06.003openalex-extracted
Smart scanning and near real-time 3D surface modeling of dynamic construction equipment from a point cloud
inferredcites →https://doi.org/10.1016/j.autcon.2015.04.001openalex-extracted
Automatic BIM component extraction from point clouds of existing buildings for sustainability applications
inferredcites →https://doi.org/10.1016/j.autcon.2016.05.010openalex-extracted
Automatic segmentation and 3D modeling of pipelines into constituent parts from laser-scan data of the built environment
inferredcites →https://doi.org/10.1016/j.autcon.2019.102837openalex-extracted
Digital twinning of existing reinforced concrete bridges from labelled point clusters
inferredcites →https://doi.org/10.1016/j.autcon.2021.103847openalex-extracted
Semantic segmentation of bridge components based on hierarchical point cloud model
inferredcites →https://doi.org/10.1016/j.autcon.2022.104459openalex-extracted
Segmentation of large-scale masonry arch bridge point clouds with a synthetic simulator and the BridgeNet neural network
inferredcites →https://doi.org/10.1016/j.autcon.2023.104838openalex-extracted
Semantic segmentation of bridge point clouds with a synthetic data augmentation strategy and graph-structured deep metric learning
inferredcites →https://doi.org/10.1016/j.engstruct.2009.06.018openalex-extracted
Historic bridge modelling using laser scanning, ground penetrating radar and finite element methods in the context of structural dynamics
inferredcites →https://doi.org/10.1016/j.isprsjprs.2015.01.011openalex-extracted
Octree-based region growing for point cloud segmentation
inferredcites →https://doi.org/10.1016/j.ophoto.2022.100023openalex-extracted
Semantic segmentation of bridge components and road infrastructure from mobile LiDAR data
inferredcites →https://doi.org/10.1016/j.tcs.2017.08.014openalex-extracted
α-Concave hull, a generalization of convex hull
inferredcites →https://doi.org/10.1061/(asce)co.1943-7862.0000655openalex-extracted
Three-Dimensional Modeling of Site Elements by Analytically Processing Image Data Contained in Site Photos
inferredcites →https://doi.org/10.1061/(asce)cp.1943-5487.0000525openalex-extracted
Semantic 3D Reconstruction of Furnished Interiors Using Laser Scanning and RFID Technology
inferredcites →https://doi.org/10.1061/(asce)cp.1943-5487.0000727openalex-extracted
IFC-Based Development of As-Built and As-Is BIMs Using Construction and Facility Inspection Data: Site-to-BIM Data Transfer Automation
inferredcites →https://doi.org/10.1088/1757-899x/1262/1/012039openalex-extracted
Some analyzes on box girders bridges - literature review
inferredcites →https://doi.org/10.1109/cvpr.2017.16openalex-extracted
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
inferredcites →https://doi.org/10.1109/ijcnn.2008.4633969openalex-extracted
ADASYN: Adaptive synthetic sampling approach for imbalanced learning
inferredcites →https://doi.org/10.1109/lgrs.2019.2918073openalex-extracted
3-D Feature Matching for Point Cloud Object Extraction
inferredcites →https://doi.org/10.1109/tpami.2018.2858826openalex-extracted
Focal Loss for Dense Object Detection
inferredcites →https://doi.org/10.1111/j.1467-8659.2007.01016.xopenalex-extracted
Efficient RANSAC for Point‐Cloud Shape Detection
inferredcites →https://doi.org/10.1111/mice.12016openalex-extracted
Data Processing of Point Clouds for Object Detection for Structural Engineering Applications
inferredcites →https://doi.org/10.1111/mice.12192openalex-extracted
Non‐Uniform B‐Spline Surface Fitting from Unordered 3D Point Clouds for As‐Built Modeling
inferredcites →https://doi.org/10.1111/mice.12306openalex-extracted
Automated Model‐Based Finding of 3D Objects in Cluttered Construction Point Cloud Models
inferredcites →https://doi.org/10.1111/mice.12407openalex-extracted
Detection of Structural Components in Point Clouds of Existing RC Bridges
inferredcites →https://doi.org/10.1145/358669.358692openalex-extracted
Random sample consensus
inferredcites →https://doi.org/10.1155/2022/2194949openalex-extracted
Digital Twin Technology for Bridge Maintenance using 3D Laser Scanning: A Review
inferredcites →https://doi.org/10.3390/buildings13112841openalex-extracted
Three-Dimensional Reconstruction of Railway Bridges Based on Unmanned Aerial Vehicle–Terrestrial Laser Scanner Point Cloud Fusion
inferredcites →https://doi.org/10.3390/rs12223757openalex-extracted
Deep-Learning-Based Classification of Point Clouds for Bridge Inspection
inferredcites →https://doi.org/10.3390/rs12223796openalex-extracted
A Decade of Modern Bridge Monitoring Using Terrestrial Laser Scanning: Review and Future Directions
inferredcites →https://doi.org/10.48550/arxiv.1706.02413openalex-extracted
PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
inferredcites →https://doi.org/10.48550/arxiv.1805.06042openalex-extracted
Automated Vision-based Bridge Component Extraction Using Multiscale Convolutional Neural Networks
inferredcites →https://doi.org/10.48550/arxiv.math/0701759openalex-extracted
Derivation of the Euler-Rodrigues formula for three-dimensional rotations from the general formula for four-dimensional rotations
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