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Two-Stage Bridge Point Cloud Segmentation by Fusing Deep Learning and Heuristic Methods

https://doi.org/10.2139/ssrn.4869671
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Outgoing claims: the papers this work cites, builds on, or argues with.

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PCT: Point cloud transformer
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.2021.103684openalex-extracted
Fully automated methodology for the delineation of railway lanes and the generation of IFC alignment models using 3D point cloud data
inferredcites →https://doi.org/10.1016/j.autcon.2022.104163openalex-extracted
Sewer defect detection from 3D point clouds using a transformer-based deep learning model
inferredcites →https://doi.org/10.1016/j.autcon.2022.104425openalex-extracted
Point cloud semantic segmentation of complex railway environments using deep learning
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.2022.104519openalex-extracted
Automated semantic segmentation of bridge components from large-scale point clouds using a weighted superpoint graph
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.autcon.2023.104865openalex-extracted
Instance and semantic segmentation of point clouds of large metallic truss bridges
inferredcites →https://doi.org/10.1016/j.autcon.2023.105176openalex-extracted
Automated production of synthetic point clouds of truss bridges for semantic and instance segmentation using deep learning models
inferredcites →https://doi.org/10.1109/cac51589.2020.9327756openalex-extracted
The Development of Digital Twin Technology Review
inferredcites →https://doi.org/10.1109/cvpr.2018.00479openalex-extracted
Large-Scale Point Cloud Semantic Segmentation with Superpoint Graphs
inferredcites →https://doi.org/10.1109/cvpr.2019.01145openalex-extracted
Meta-Learning Convolutional Neural Architectures for Multi-Target Concrete Defect Classification With the COncrete DEfect BRidge IMage Dataset
inferredcites →https://doi.org/10.1109/cvpr42600.2020.01112openalex-extracted
RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds
inferredcites →https://doi.org/10.1109/jstars.2021.3060568openalex-extracted
Toward Building and Civil Infrastructure Reconstruction From Point Clouds: A Review on Data and Key Techniques
inferredcites →https://doi.org/10.1109/mgrs.2019.2937630openalex-extracted
Linking Points With Labels in 3D: A Review of Point Cloud Semantic Segmentation
inferredcites →https://doi.org/10.1109/tcyb.2022.3159815openalex-extracted
FG-Net: A Fast and Accurate Framework for Large-Scale LiDAR Point Cloud Understanding
inferredcites →https://doi.org/10.1109/tpami.2016.2609426openalex-extracted
Ranking Saliency
inferredcites →https://doi.org/10.1109/tpami.2020.3005434openalex-extracted
Deep Learning for 3D Point Clouds: A Survey
inferredcites →https://doi.org/10.1111/mice.12407openalex-extracted
Detection of Structural Components in Point Clouds of Existing RC Bridges
inferredcites →https://doi.org/10.1145/3326362openalex-extracted
Dynamic Graph CNN for Learning on Point Clouds
inferredcites →https://doi.org/10.1155/2023/3733799openalex-extracted
Fine-Grained Point Cloud Semantic Segmentation of Complex Railway Bridge Scenes from UAVs Using Improved DGCNN
inferredcites →https://doi.org/10.1609/aaai.v34i07.6994openalex-extracted
JSNet: Joint Instance and Semantic Segmentation of 3D Point Clouds
inferredcites →https://doi.org/10.3390/rs12223757openalex-extracted
Deep-Learning-Based Classification of Point Clouds for Bridge Inspection
inferredcites →https://doi.org/10.3390/rs15184504openalex-extracted
A Handheld LiDAR-Based Semantic Automatic Segmentation Method for Complex Railroad Line Model Reconstruction
inferredcites →https://doi.org/10.3390/s19194188openalex-extracted
Deep Learning on Point Clouds and Its Application: A Survey
inferredcites →https://doi.org/10.3390/s23177349openalex-extracted
The Concept of Creating Digital Twins of Bridges Using Load Tests
inferredcites →https://doi.org/10.48550/arxiv.1512.03012openalex-extracted
ShapeNet: An Information-Rich 3D Model Repository
inferredcites →https://doi.org/10.5220/0010207908910900openalex-extracted
Sewer Defect Classification using Synthetic Point Clouds
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