Research graph
References from Exploiting structural repetition for synthetic training data generation in LiDAR point cloud segmentation. Local targets link to admitted publications; unresolved targets remain external evidence.
Unresolved reference
2018 · External reference
Unresolved reference
2018 · External reference
Automating the retrospective generation of As-is BIM models using machine learning
10.1016/j.autcon.2023.104937 · 2023 · External reference
Unresolved reference
2005 · External reference
An overview of lidar imaging systems for autonomous vehicles
10.3390/app9194093 · 2019 · External reference
Automatic creation of semantically rich 3D building models from laser scanner data
10.1016/j.autcon.2012.10.006 · 2013 · External reference
Automated material-aware BIM generation using deep learning for comprehensive indoor element reconstruction
10.1016/j.autcon.2025.106196 · 2025 · External reference
Indoor 3D point cloud reconstruction for scan-to-BIM automation
10.1016/j.autcon.2026.106853 · 2026 · External reference
Deviation analysis method for the assessment of the quality of the as-is Building Information Models generated from point cloud data
10.1016/j.autcon.2013.06.003 · 2013 · External reference
BIM generation from 3D point clouds by combining 3D deep learning and improved morphological approach
10.1016/j.autcon.2022.104422 · 2022 · External reference
Deep learning–based scan-to-BIM automation and object scope expansion using a low-cost 3D scan data
2024 · External reference
BIM module for deep learning-driven parametric IFC reconstruction
10.5194/isprs-archives-xlviii-2-w8-2024-403-2024 · 2024 · External reference
A survey on self-supervised learning: Algorithms, applications, and future trends
10.1109/tpami.2024.3415112 · 2024 · External reference
An extensive review of tools for manual annotation of documents
10.1093/bib/bbz130 · 2021 · External reference
Deep learning for 3D point clouds: A survey
10.1109/tpami.2020.3005434 · 2021 · External reference
Quadruped robots in construction automation: A comprehensive review of applications, localization, and site-level operations
10.3390/buildings16050962 · 2026 · External reference
Optical measurement system for monitoring railway infrastructure—A review
10.3390/app14198801 · 2024 · External reference
Automated detection and decomposition of railway tunnels from Mobile Laser Scanning Datasets
10.1016/j.autcon.2018.09.014 · 2018 · External reference
Automatic creation of as-is building information model from single-track railway tunnel point clouds
10.1016/j.autcon.2019.102911 · 2019 · External reference
Semantic segmentation of point clouds with pointnet and KPConv architectures applied to railway tunnels
2020 · External reference
Automatic point cloud semantic segmentation of complex railway environments
10.3390/rs13122332 · 2021 · External reference
UnrollingNet: An attention-based deep learning approach for the segmentation of large-scale point clouds of tunnels
10.1016/j.autcon.2022.104456 · 2022 · External reference
Deep learning for large-scale point cloud segmentation in tunnels considering causal inference
10.1016/j.autcon.2023.104915 · 2023 · External reference
RailPC: A large-scale railway point cloud semantic segmentation dataset
10.1049/cit2.12349 · 2024 · External reference
STSD: A large-scale benchmark for semantic segmentation of subway tunnel point cloud
10.1016/j.tust.2024.105829 · 2024 · External reference
Seg2Tunnel: A hierarchical point cloud dataset and benchmarks for segmentation of segmental tunnel linings
10.1016/j.tust.2024.105735 · 2024 · External reference
Blainder—A blender AI add-on for generation of semantically labeled depth-sensing data
10.3390/s21062144 · 2021 · External reference
From bim to pointcloud: Automatic generation of labeled indoor pointcloud
10.5194/isprs-archives-xliii-b5-2022-73-2022 · 2022 · External reference
Graphic simulation framework of railway scenarios for LiDAR dataset generation
2022 · External reference
Generating synthetic point clouds of sewer networks: An initial investigation
2020 · External reference
Procedural generation of tunnel networks for unsupervised training and testing in underground applications
2024 · External reference
Methode zur generierung multimodaler synthetischer daten aus parametrischen BIM-modellen zur nutzung in KI-systemen
2023 · External reference
Controlroom3D: Room generation using semantic proxy rooms
2024 · External reference
Random bridge generator as a platform for developing computer vision-based structural inspection algorithms
2024 · External reference
3D object detection on synthetic point clouds for railway applications
2022 · External reference
Sewer defect classification using synthetic point clouds
2021 · External reference
Game engine-driven synthetic point cloud generation method for LiDAR-based defect detection in sewers
10.1016/j.tust.2025.106755 · 2025 · External reference
Synthetic environments for vision-based structural condition assessment of Japanese high-speed railway viaducts
10.1016/j.ymssp.2021.107850 · 2021 · External reference
TrainSim: A railway simulation framework for LiDAR and camera dataset generation
10.1109/tits.2023.3297728 · 2023 · External reference
Reducing domain shift in synthetic data augmentation for semantic segmentation of 3D point clouds
2022 · External reference
Enhancing point cloud semantic segmentation in the data-scarce domain of industrial plants through synthetic data
10.1111/mice.13153 · 2024 · External reference
Automatic generation of synthetic heritage point clouds: Analysis and segmentation based on shape grammar for historical vaults
10.1016/j.culher.2023.10.003 · 2024 · External reference
3D bridge segmentation using semi-supervised domain adaptation
10.1016/j.autcon.2025.106021 · 2025 · External reference
STPLS3D: A large-scale synthetic and real aerial photogrammetry 3D point cloud dataset
2022 · External reference
Semantic segmentation of point clouds of building interiors with deep learning: Augmenting training datasets with synthetic BIM-based point clouds
10.1016/j.autcon.2020.103144 · 2020 · External reference
BIM-driven data augmentation method for semantic segmentation in superpoint-based deep learning network
10.1016/j.autcon.2022.104373 · 2022 · External reference
Skeleton-guided generation of synthetic noisy point clouds from as-built BIM to improve indoor scene understanding
10.1016/j.autcon.2023.105076 · 2023 · External reference
3D as-built modeling from incomplete point clouds using connectivity relations
10.1016/j.autcon.2021.103855 · 2021 · External reference
Preparation of synthetic as-damaged models for post-earthquake BIM reconstruction research
10.1061/(asce)cp.1943-5487.0000500 · 2016 · External reference
SqueezeSeg: Convolutional neural nets with recurrent CRF for real-time road-object segmentation from 3D LiDAR point cloud
2018 · External reference
SqueezeSegV2: Improved model structure and unsupervised domain adaptation for road-object segmentation from a LiDAR point cloud
2019 · External reference
A systematic review and evaluation of synthetic simulated data generation strategies for deep learning applications in construction
10.1016/j.aei.2024.102699 · 2024 · External reference
Domain randomization for transferring deep neural networks from simulation to the real world
2017 · External reference
Structured domain randomization: Bridging the reality gap by context-aware synthetic data
2019 · External reference
Unresolved reference
External reference
Unresolved reference
2002 · External reference
Improving noise
2002 · External reference
Poisson surface reconstruction
2006 · External reference
Automated training data creation for semantic segmentation of 3D point clouds
10.5194/isprs-archives-xlvi-5-w1-2022-59-2022 · 2022 · External reference
An efficient method for producing deep learning point cloud datasets based on BIM 3D model and computer simulation
2022 · External reference
Deep learning-based pipe segmentation and geometric reconstruction from poorly scanned point clouds using BIM-driven data alignment
10.1016/j.autcon.2025.106071 · 2025 · External reference
Fast Poisson disk sampling in arbitrary dimensions
2007 · External reference
Automatic generation of point cloud synthetic dataset for historical building representation
2019 · External reference
Automated BIM-to-scan point cloud semantic segmentation using a domain adaptation network with hybrid attention and whitening (DawNet)
10.1016/j.autcon.2024.105473 · 2024 · External reference
Exploiting BIM objects for synthetic data generation toward indoor point cloud classification using deep learning
10.1061/(asce)cp.1943-5487.0001039 · 2022 · External reference
Webots™: Professional mobile robot simulation
10.5772/5618 · 2004 · External reference
Design and use paradigms for Gazebo, an open-source multi-robot simulator
2004 · External reference
Unresolved reference
2025 · External reference
Unresolved reference
2025 · External reference
CARLA: An open urban driving simulator
2017 · External reference
Unresolved reference
2024 · External reference
Automatic generation of synthetic LiDAR point clouds for 3-D data analysis
10.1109/tim.2019.2906416 · 2019 · External reference
GPU rasterization-based 3D LiDAR simulation for deep learning
10.3390/s23198130 · 2023 · External reference
Unresolved reference
External reference
Simulating LiDAR to create training data for machine learning on 3D point clouds
2022 · External reference
BlenSor: Blender sensor simulation toolbox
2011 · External reference
Visual programming simulator for producing realistic labeled point clouds from digital infrastructure models
10.1016/j.autcon.2023.105126 · 2023 · External reference
Role of simulated lidar data for training 3D deep learning models: An exhaustive analysis
10.1007/s12524-024-01905-2 · 2024 · External reference
Unresolved reference
2025 · External reference
LiMOX—A point cloud lidar model toolbox based on NVIDIA OptiX ray tracing engine
10.3390/s24061846 · 2024 · External reference
LESS LiDAR: A full-waveform and discrete-return multispectral LiDAR simulator based on ray tracing algorithm
10.3390/rs15184529 · 2023 · External reference
Virtual laser scanning with HELIOS++: A novel take on ray tracing-based simulation of topographic full-waveform 3D laser scanning
10.1016/j.rse.2021.112772 · 2022 · External reference
10.31030/3492066
10.31030/3492066 · External reference
Unresolved reference
2026 · External reference
Semantic classification in uncolored 3D point clouds using multiscale features
2023 · External reference
Unresolved reference
2025 · External reference
Scan2BIM-NET: Deep learning method for segmentation of point clouds for scan-to-BIM
10.1061/(asce)co.1943-7862.0002132 · 2021 · External reference
Deep learning based semantic segmentation for BIM model generation from RGB-D sensors
2024 · External reference
From scans to parametric BIM: An enhanced framework using synthetic data augmentation and parametric modeling for highway bridges
10.1061/jccee5.cpeng-5640 · 2024 · External reference
Automated Scan-to-BIM: A deep learning-based framework for indoor environments with complex furniture elements
2025 · External reference
Point transformer V3: Simpler, faster, stronger
2024 · External reference
Sonata: Self-supervised learning of reliable point representations
2025 · External reference
OA-CNNs: Omni-adaptive sparse CNNs for 3D semantic segmentation
2024 · External reference
Unresolved reference
2023 · External reference
4D spatio-temporal ConvNets: Minkowski convolutional neural networks
2019 · External reference
Unresolved reference
2022 · External reference
U-Net: Convolutional networks for biomedical image segmentation
2015 · External reference
The lovasz-softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks
2018 · External reference
Unresolved reference
2018 · External reference
3DSES: An indoor lidar point cloud segmentation dataset with real and pseudo-labels from a 3D model
2025 · External reference
Unresolved reference
2022 · External reference
PyTorch: An imperative style, high-performance deep learning library
2019 · External reference
Unresolved reference
2019 · External reference
Super-convergence: Very fast training of neural networks using large learning rates
2019 · External reference
Gradient surgery for multi-task learning
2020 · External reference
Domain generalization via gradient surgery
2021 · External reference
Learning without forgetting
10.1109/tpami.2017.2773081 · 2018 · External reference
Gradient episodic memory for continual learning
2017 · External reference
Unresolved reference
2016 · External reference
Role of simulated lidar data for training 3D deep learning models: An exhaustive analysis
10.1007/s12524-024-01905-2 · ExternalCitation · doi-reference
A systematic review and evaluation of synthetic simulated data generation strategies for deep learning applications in construction
10.1016/j.aei.2024.102699 · ExternalCitation · doi-reference
Automatic creation of semantically rich 3D building models from laser scanner data
10.1016/j.autcon.2012.10.006 · ExternalCitation · doi-reference
Deviation analysis method for the assessment of the quality of the as-is Building Information Models generated from point cloud data
10.1016/j.autcon.2013.06.003 · ExternalCitation · doi-reference
Automated detection and decomposition of railway tunnels from Mobile Laser Scanning Datasets
10.1016/j.autcon.2018.09.014 · ExternalCitation · doi-reference
Automatic creation of as-is building information model from single-track railway tunnel point clouds
10.1016/j.autcon.2019.102911 · ExternalCitation · doi-reference
Semantic segmentation of point clouds of building interiors with deep learning: Augmenting training datasets with synthetic BIM-based point clouds
10.1016/j.autcon.2020.103144 · ExternalCitation · doi-reference
3D as-built modeling from incomplete point clouds using connectivity relations
10.1016/j.autcon.2021.103855 · ExternalCitation · doi-reference
BIM-driven data augmentation method for semantic segmentation in superpoint-based deep learning network
10.1016/j.autcon.2022.104373 · ExternalCitation · doi-reference
BIM generation from 3D point clouds by combining 3D deep learning and improved morphological approach
10.1016/j.autcon.2022.104422 · ExternalCitation · doi-reference
UnrollingNet: An attention-based deep learning approach for the segmentation of large-scale point clouds of tunnels
10.1016/j.autcon.2022.104456 · ExternalCitation · doi-reference
Deep learning for large-scale point cloud segmentation in tunnels considering causal inference
10.1016/j.autcon.2023.104915 · ExternalCitation · doi-reference
Automating the retrospective generation of As-is BIM models using machine learning
10.1016/j.autcon.2023.104937 · ExternalCitation · doi-reference
Skeleton-guided generation of synthetic noisy point clouds from as-built BIM to improve indoor scene understanding
10.1016/j.autcon.2023.105076 · ExternalCitation · doi-reference
Visual programming simulator for producing realistic labeled point clouds from digital infrastructure models
10.1016/j.autcon.2023.105126 · ExternalCitation · doi-reference
Automated BIM-to-scan point cloud semantic segmentation using a domain adaptation network with hybrid attention and whitening (DawNet)
10.1016/j.autcon.2024.105473 · ExternalCitation · doi-reference
3D bridge segmentation using semi-supervised domain adaptation
10.1016/j.autcon.2025.106021 · ExternalCitation · doi-reference
Deep learning-based pipe segmentation and geometric reconstruction from poorly scanned point clouds using BIM-driven data alignment
10.1016/j.autcon.2025.106071 · ExternalCitation · doi-reference
Automated material-aware BIM generation using deep learning for comprehensive indoor element reconstruction
10.1016/j.autcon.2025.106196 · ExternalCitation · doi-reference
Indoor 3D point cloud reconstruction for scan-to-BIM automation
10.1016/j.autcon.2026.106853 · ExternalCitation · doi-reference
Automatic generation of synthetic heritage point clouds: Analysis and segmentation based on shape grammar for historical vaults
10.1016/j.culher.2023.10.003 · ExternalCitation · doi-reference
Virtual laser scanning with HELIOS++: A novel take on ray tracing-based simulation of topographic full-waveform 3D laser scanning
10.1016/j.rse.2021.112772 · ExternalCitation · doi-reference
Seg2Tunnel: A hierarchical point cloud dataset and benchmarks for segmentation of segmental tunnel linings
10.1016/j.tust.2024.105735 · ExternalCitation · doi-reference
STSD: A large-scale benchmark for semantic segmentation of subway tunnel point cloud
10.1016/j.tust.2024.105829 · ExternalCitation · doi-reference
Game engine-driven synthetic point cloud generation method for LiDAR-based defect detection in sewers
10.1016/j.tust.2025.106755 · ExternalCitation · doi-reference
Synthetic environments for vision-based structural condition assessment of Japanese high-speed railway viaducts
10.1016/j.ymssp.2021.107850 · ExternalCitation · doi-reference
RailPC: A large-scale railway point cloud semantic segmentation dataset
10.1049/cit2.12349 · ExternalCitation · doi-reference
Scan2BIM-NET: Deep learning method for segmentation of point clouds for scan-to-BIM
10.1061/(asce)co.1943-7862.0002132 · ExternalCitation · doi-reference
Preparation of synthetic as-damaged models for post-earthquake BIM reconstruction research
10.1061/(asce)cp.1943-5487.0000500 · ExternalCitation · doi-reference
Exploiting BIM objects for synthetic data generation toward indoor point cloud classification using deep learning
10.1061/(asce)cp.1943-5487.0001039 · ExternalCitation · doi-reference
From scans to parametric BIM: An enhanced framework using synthetic data augmentation and parametric modeling for highway bridges
10.1061/jccee5.cpeng-5640 · ExternalCitation · doi-reference
An extensive review of tools for manual annotation of documents
10.1093/bib/bbz130 · ExternalCitation · doi-reference
Automatic generation of synthetic LiDAR point clouds for 3-D data analysis
10.1109/tim.2019.2906416 · ExternalCitation · doi-reference
TrainSim: A railway simulation framework for LiDAR and camera dataset generation
10.1109/tits.2023.3297728 · ExternalCitation · doi-reference
Learning without forgetting
10.1109/tpami.2017.2773081 · ExternalCitation · doi-reference
Deep learning for 3D point clouds: A survey
10.1109/tpami.2020.3005434 · ExternalCitation · doi-reference
A survey on self-supervised learning: Algorithms, applications, and future trends
10.1109/tpami.2024.3415112 · ExternalCitation · doi-reference
Enhancing point cloud semantic segmentation in the data-scarce domain of industrial plants through synthetic data
10.1111/mice.13153 · ExternalCitation · doi-reference
10.31030/3492066
10.31030/3492066 · ExternalCitation · doi-reference
Optical measurement system for monitoring railway infrastructure—A review
10.3390/app14198801 · ExternalCitation · doi-reference
An overview of lidar imaging systems for autonomous vehicles
10.3390/app9194093 · ExternalCitation · doi-reference
Quadruped robots in construction automation: A comprehensive review of applications, localization, and site-level operations
10.3390/buildings16050962 · ExternalCitation · doi-reference
Automatic point cloud semantic segmentation of complex railway environments
10.3390/rs13122332 · ExternalCitation · doi-reference
LESS LiDAR: A full-waveform and discrete-return multispectral LiDAR simulator based on ray tracing algorithm
10.3390/rs15184529 · ExternalCitation · doi-reference
Blainder—A blender AI add-on for generation of semantically labeled depth-sensing data
10.3390/s21062144 · ExternalCitation · doi-reference
GPU rasterization-based 3D LiDAR simulation for deep learning
10.3390/s23198130 · ExternalCitation · doi-reference
LiMOX—A point cloud lidar model toolbox based on NVIDIA OptiX ray tracing engine
10.3390/s24061846 · ExternalCitation · doi-reference
From bim to pointcloud: Automatic generation of labeled indoor pointcloud
10.5194/isprs-archives-xliii-b5-2022-73-2022 · ExternalCitation · doi-reference
Automated training data creation for semantic segmentation of 3D point clouds
10.5194/isprs-archives-xlvi-5-w1-2022-59-2022 · ExternalCitation · doi-reference
BIM module for deep learning-driven parametric IFC reconstruction
10.5194/isprs-archives-xlviii-2-w8-2024-403-2024 · ExternalCitation · doi-reference
Webots™: Professional mobile robot simulation
10.5772/5618 · ExternalCitation · doi-reference
