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Automated Point Cloud Semantic Segmentation of Ancient Timber Architecture Components Using a Global Attention Network

https://doi.org/10.2139/ssrn.4892435
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inferredcites →https://doi.org/10.1007/978-3-030-01225-0_37openalex-snapshot
Fully-Convolutional Point Networks for Large-Scale Point Clouds
inferredcites →https://doi.org/10.1007/978-3-030-01240-3_17openalex-snapshot
PSANet: Point-wise Spatial Attention Network for Scene Parsing
inferredcites →https://doi.org/10.1007/978-3-031-73337-6_25openalex-snapshot
Reliability in Semantic Segmentation: Can We Use Synthetic Data?
inferredcites →https://doi.org/10.1007/978-3-642-40602-7_39openalex-snapshot
Spatial Pattern Templates for Recognition of Objects with Regular Structure
inferredcites →https://doi.org/10.1016/j.aei.2015.01.001openalex-snapshot
State of research in automatic as-built modelling
inferredcites →https://doi.org/10.1016/j.aei.2020.101121openalex-snapshot
CLOI-NET: Class segmentation of industrial facilities’ point cloud datasets
inferredcites →https://doi.org/10.1016/j.autcon.2019.103058openalex-snapshot
Transfer Learning in urban object classification: Online images to recognize point clouds
inferredcites →https://doi.org/10.1016/j.autcon.2020.103131openalex-snapshot
Automated digital modeling of existing buildings: A review of visual object recognition methods
inferredcites →https://doi.org/10.1016/j.autcon.2020.103144openalex-snapshot
Semantic segmentation of point clouds of building interiors with deep learning: Augmenting training datasets with synthetic BIM-based point clouds
inferredcites →https://doi.org/10.1016/j.autcon.2020.103232openalex-snapshot
Point cloud segmentation and classification of structural elements in multi-planar masonry building facades
inferredcites →https://doi.org/10.1016/j.autcon.2021.103856openalex-snapshot
Semantic interpretation of architectural and archaeological geometries: Point cloud segmentation for HBIM parameterisation
inferredcites →https://doi.org/10.1016/j.culher.2023.10.003openalex-snapshot
Automatic generation of synthetic heritage point clouds: Analysis and segmentation based on shape grammar for historical vaults
inferredcites →https://doi.org/10.1016/j.measurement.2018.12.073openalex-snapshot
Optical measurements based on practical methods for detecting time-wise morphing structures
inferredcites →https://doi.org/10.1016/j.measurement.2020.108291openalex-snapshot
Validation of terrestrial laser scanning and artificial intelligence for measuring deformations of cultural heritage structures
inferredcites →https://doi.org/10.1016/j.measurement.2022.112321openalex-snapshot
3D spatial measurement for model reconstruction: A review
inferredcites →https://doi.org/10.1016/j.measurement.2024.114893openalex-snapshot
Corrosion segmentation method of concrete drainage pipes based on point transformer
inferredcites →https://doi.org/10.1109/cvpr.2016.442openalex-snapshot
Understanding RealWorld Indoor Scenes with Synthetic Data
inferredcites →https://doi.org/10.1109/cvpr.2017.16openalex-snapshot
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
inferredcites →https://doi.org/10.1109/cvpr.2018.00278openalex-snapshot
Recurrent Slice Networks for 3D Segmentation of Point Clouds
inferredcites →https://doi.org/10.1109/cvpr.2018.00479openalex-snapshot
Large-Scale Point Cloud Semantic Segmentation with Superpoint Graphs
inferredcites →https://doi.org/10.1109/cvpr.2018.00484openalex-snapshot
Attentional ShapeContextNet for Point Cloud Recognition
inferredcites →https://doi.org/10.1109/cvpr.2018.00813openalex-snapshot
Non-local Neural Networks
inferredcites →https://doi.org/10.1109/cvpr.2018.00961openalex-snapshot
3D Semantic Segmentation with Submanifold Sparse Convolutional Networks
inferredcites →https://doi.org/10.1109/cvpr.2018.00979openalex-snapshot
SO-Net: Self-Organizing Network for Point Cloud Analysis
inferredcites →https://doi.org/10.1109/cvpr.2019.00326openalex-snapshot
Dual Attention Network for Scene Segmentation
inferredcites →https://doi.org/10.1109/cvpr.2019.00910openalex-snapshot
Relation-Shape Convolutional Neural Network for Point Cloud Analysis
inferredcites →https://doi.org/10.1109/cvpr.2019.01054openalex-snapshot
Graph Attention Convolution for Point Cloud Semantic Segmentation
inferredcites →https://doi.org/10.1109/cvpr42600.2020.00325openalex-snapshot
Relation-Aware Global Attention for Person Re-Identification
inferredcites →https://doi.org/10.1109/cvpr42600.2020.01112openalex-snapshot
RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds
inferredcites →https://doi.org/10.1109/iccv.2017.292openalex-snapshot
SceneNet RGB-D: Can 5M Synthetic Images Beat Generic ImageNet Pre-training on Indoor Segmentation?
inferredcites →https://doi.org/10.1109/iccv.2019.00859openalex-snapshot
VV-Net: Voxel VAE Net With Group Convolutions for Point Cloud Segmentation
inferredcites →https://doi.org/10.1109/iccv.2019.00939openalex-snapshot
SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences
inferredcites →https://doi.org/10.1109/icpr.2016.7900038openalex-snapshot
Point cloud labeling using 3D Convolutional Neural Network
inferredcites →https://doi.org/10.1109/tim.2019.2906416openalex-snapshot
Automatic Generation of Synthetic LiDAR Point Clouds for 3-D Data Analysis
inferredcites →https://doi.org/10.1109/tpami.2012.252openalex-snapshot
Parsing Facades with Shape Grammars and Reinforcement Learning
inferredcites →https://doi.org/10.1109/tpami.2020.3005434openalex-snapshot
Deep Learning for 3D Point Clouds: A Survey
inferredcites →https://doi.org/10.1111/mice.12407openalex-snapshot
Detection of Structural Components in Point Clouds of Existing RC Bridges
inferredcites →https://doi.org/10.1145/3197517.3201301openalex-snapshot
Point convolutional neural networks by extension operators
inferredcites →https://doi.org/10.1145/3272127.3275110openalex-snapshot
Monte Carlo convolution for learning on non-uniformly sampled point clouds
inferredcites →https://doi.org/10.1145/3326362openalex-snapshot
Dynamic Graph CNN for Learning on Point Clouds
inferredcites →https://doi.org/10.1145/3409262openalex-snapshot
Learning from Synthetic Point Cloud Data for Historical Buildings Semantic Segmentation
inferredcites →https://doi.org/10.1186/s40494-022-00844-wopenalex-snapshot
Implementing PointNet for point cloud segmentation in the heritage context
inferredcites →https://doi.org/10.2312/3dor.20171047openalex-snapshot
Unstructured Point Cloud Semantic Labeling Using Deep Segmentation Networks
inferredcites →https://doi.org/10.3390/rs8040270openalex-snapshot
Fine Surveying and 3D Modeling Approach for Wooden Ancient Architecture via Multiple Laser Scanner Integration
inferredcites →https://doi.org/10.48550/arxiv.1702.01105openalex-snapshot
Joint 2D-3D-Semantic Data for Indoor Scene Understanding
inferredcites →https://doi.org/10.48550/arxiv.1804.00103openalex-snapshot
A LiDAR Point Cloud Generator: from a Virtual World to Autonomous Driving
inferredcites →https://doi.org/10.48550/arxiv.1904.07601openalex-snapshot
Relation-Shape Convolutional Neural Network for Point Cloud Analysis
inferredcites →https://doi.org/10.5194/isprs-archives-xlvi-m-1-2021-321-2021openalex-snapshot
AN EXTRACTION METHOD FOR ROOF POINT CLOUD OF ANCIENT BUILDING USING DEEP LEARNING FRAMEWORK
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