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Attention-enhanced sampling point cloud network (ASPCNet) for efficient 3D tunnel semantic segmentation

https://doi.org/10.1016/j.autcon.2022.104667
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Point cloud semantic scene segmentation based on coordinate convolution
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A Heuristic Sampling Method for Maintaining the Probability Distribution
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PCT: Point cloud transformer
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Automatic defect detection of metro tunnel surfaces using a vision-based inspection system
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Sensitivity analysis of structural health risk in operational tunnels
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Damage detection and quantitative analysis of shield tunnel structure
inferredcites →https://doi.org/10.1016/j.autcon.2021.103847openalex-snapshot
Semantic segmentation of bridge components based on hierarchical point cloud model
inferredcites →https://doi.org/10.1016/j.autcon.2021.103874openalex-snapshot
Automated semantic segmentation of industrial point clouds using ResPointNet++
inferredcites →https://doi.org/10.1016/j.autcon.2021.103934openalex-snapshot
A deep learning-based approach for refined crack evaluation from shield tunnel lining images
inferredcites →https://doi.org/10.1016/j.autcon.2022.104163openalex-snapshot
Sewer defect detection from 3D point clouds using a transformer-based deep learning model
inferredcites →https://doi.org/10.1016/j.autcon.2022.104187openalex-snapshot
An encoder-decoder deep learning method for multi-class object segmentation from 3D tunnel point clouds
inferredcites →https://doi.org/10.1016/j.autcon.2022.104256openalex-snapshot
Multi-objective optimization for improved project management: Current status and future directions
inferredcites →https://doi.org/10.1016/j.autcon.2022.104456openalex-snapshot
UnrollingNet: An attention-based deep learning approach for the segmentation of large-scale point clouds of tunnels
inferredcites →https://doi.org/10.1016/j.cag.2020.06.001openalex-snapshot
Attention-based relation and context modeling for point cloud semantic segmentation
inferredcites →https://doi.org/10.1016/j.conbuildmat.2019.117367openalex-snapshot
Image-based concrete crack detection in tunnels using deep fully convolutional networks
inferredcites →https://doi.org/10.1016/j.jtrangeo.2011.09.006openalex-snapshot
Transportation infrastructure impacts on firm location: the effect of a new metro line in the suburbs of Madrid
inferredcites →https://doi.org/10.1016/j.neucom.2019.10.116openalex-snapshot
Multi-view semantic learning network for point cloud based 3D object detection
inferredcites →https://doi.org/10.1016/j.neucom.2020.10.086openalex-snapshot
PointVGG: Graph convolutional network with progressive aggregating features on point clouds
inferredcites →https://doi.org/10.1016/j.patcog.2020.107446openalex-snapshot
Point attention network for semantic segmentation of 3D point clouds
inferredcites →https://doi.org/10.1016/j.patrec.2020.03.021openalex-snapshot
Feature fusion network based on attention mechanism for 3D semantic segmentation of point clouds
inferredcites →https://doi.org/10.1016/j.tust.2019.103009openalex-snapshot
Factors in the development of urban underground space surrounding metro stations: A case study of Osaka, Japan
inferredcites →https://doi.org/10.1016/j.tust.2019.103156openalex-snapshot
Deep learning–based image instance segmentation for moisture marks of shield tunnel lining
inferredcites →https://doi.org/10.1088/1757-899x/428/1/012022openalex-snapshot
Weighted Focal Loss: An Effective Loss Function to Overcome Unbalance Problem of Chest X-ray14
inferredcites →https://doi.org/10.1109/83.623193openalex-snapshot
The farthest point strategy for progressive image sampling
inferredcites →https://doi.org/10.1109/access.2019.2958671openalex-snapshot
A Review of Deep Learning-Based Semantic Segmentation for Point Cloud
inferredcites →https://doi.org/10.1109/cvpr.2016.207openalex-snapshot
Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network
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.2019.00344openalex-snapshot
Modeling Point Clouds With Self-Attention and Gumbel Subset Sampling
inferredcites →https://doi.org/10.1109/cvpr.2019.00571openalex-snapshot
PointWeb: Enhancing Local Neighborhood Features for Point Cloud Processing
inferredcites →https://doi.org/10.1109/cvpr.2019.01054openalex-snapshot
Graph Attention Convolution for Point Cloud Semantic Segmentation
inferredcites →https://doi.org/10.1109/iccv.2019.00764openalex-snapshot
Dynamic Points Agglomeration for Hierarchical Point Sets Learning
inferredcites →https://doi.org/10.1109/iccv.2019.00825openalex-snapshot
Unsupervised Multi-Task Feature Learning on Point Clouds
inferredcites →https://doi.org/10.1109/icme.2019.00064openalex-snapshot
Justlookup: One Millisecond Deep Feature Extraction for Point Clouds By Lookup Tables
inferredcites →https://doi.org/10.1111/mice.12731openalex-snapshot
An optimization strategy to improve the deep learning‐based recognition model of leakage in shield tunnels
inferredcites →https://doi.org/10.1145/2688073openalex-snapshot
Proceedings of the 2015 Conference on Innovations in Theoretical Computer Science
inferredcites →https://doi.org/10.1145/3326362openalex-snapshot
Dynamic Graph CNN for Learning on Point Clouds
inferredcites →https://doi.org/10.1145/3343031openalex-snapshot
MM '19: Proceedings of the 27th ACM International Conference on Multimedia, MM 2019, Nice, France, October 21-25, 2019
inferredcites →https://doi.org/10.3390/rs12061005openalex-snapshot
Point Cloud Semantic Segmentation Using a Deep Learning Framework for Cultural Heritage

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4573196openalex-extracted
Automatic Structure Segmentation and Classification of Complex Railway Tunnels from Mobile Laser Scanning Data
inferred← citeshttps://doi.org/10.2139/ssrn.4707244openalex-extracted
An Intelligent Detection Method for Precise Analysis of Shield Tunnel Lining Joints Through the Enhanced Faster Region-Based Convolutional Neural Network
inferred← citeshttps://doi.org/10.1016/j.aei.2025.103609openalex-snapshot
FESS-SAM: Full-element semantic segmentation of tunnel linear array images based on the segment anything model
inferred← citeshttps://doi.org/10.1016/j.aei.2026.104952openalex-snapshot
A multi-region joint registration algorithm for supporting tunnel deformation monitoring
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Human-inspired zero-shot semantic segmentation of construction point clouds based on adaptive observation and MLLM reasoning
inferred← citeshttps://doi.org/10.1016/j.autcon.2023.105108openalex-snapshot
3D reconstruction of large-scale scaffolds with synthetic data generation and an upsampling adversarial network
inferred← citeshttps://doi.org/10.1016/j.autcon.2023.105131openalex-snapshot
Dual attention-based deep learning network for multi-class object semantic segmentation of tunnel point clouds
inferred← citeshttps://doi.org/10.1016/j.autcon.2024.105473openalex-snapshot
Automated BIM-to-scan point cloud semantic segmentation using a domain adaptation network with hybrid attention and whitening (DawNet)
inferred← citeshttps://doi.org/10.1016/j.autcon.2024.105769openalex-snapshot
Deep learning applications for point clouds in the construction industry
inferred← citeshttps://doi.org/10.1016/j.autcon.2024.105799openalex-snapshot
Dual hierarchical attention-enhanced transfer learning for semantic segmentation of point clouds in building scene understanding
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Inner wall defect detection in oil and gas pipelines using point cloud data segmentation
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Dual-attention deep learning model for real-time advance rate prediction in TBM operations
inferred← citeshttps://doi.org/10.1016/j.autcon.2025.106336openalex-snapshot
2D–3D fusion approach for improved point cloud segmentation
inferred← citeshttps://doi.org/10.1016/j.autcon.2026.106799openalex-snapshot
Margin-aware maximum classifier discrepancy for BIM-to-scan semantic segmentation of building point clouds
inferred← citeshttps://doi.org/10.1016/j.autcon.2026.106908openalex-snapshot
Serialized point cloud segmentation with dilated patchwise attention for generating geometric twins of shield metro tunnels
inferred← citeshttps://doi.org/10.1016/j.autcon.2026.107221openalex-snapshot
Contrastive boundary learning for automated quality inspection of shield tunnel segment assembly
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Occlusion-aware hybrid learning framework for point cloud understanding in building mechanical, electrical, and plumbing systems
inferred← citeshttps://doi.org/10.1016/j.engappai.2024.108460openalex-snapshot
Multi-visual modality micro drone-based structural damage detection
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Vector attention-based point cloud network for semantic segmentation of sewer sonar data
inferred← citeshttps://doi.org/10.1016/j.engappai.2026.113909openalex-snapshot
Few-shot semantic segmentation for clearance intrusion risk detection in metro tunnel point clouds
inferred← citeshttps://doi.org/10.1016/j.inffus.2024.102575openalex-snapshot
Advancements in point cloud-based 3D defect classification and segmentation for industrial systems: A comprehensive survey
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Advancing 3D point cloud understanding through deep transfer learning: A comprehensive survey
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Point cloud extraction for coal mine roadways based on a lightweight deep learning network
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Seg2Tunnel: A hierarchical point cloud dataset and benchmarks for segmentation of segmental tunnel linings
inferred← citeshttps://doi.org/10.1016/j.tust.2024.106144openalex-snapshot
P-CSF: Polar coordinate cloth simulation filtering algorithm for multi-type tunnel point clouds
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SAM4Tun: No-training model for tunnel lining point cloud component segmentation
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Unsupervised seepage segmentation pipeline based on point cloud projection with large vision model
inferred← citeshttps://doi.org/10.1016/j.tust.2025.106605openalex-snapshot
Deep learning-based point cloud semantic segmentation for tunnel face excavation areas in drilling and blasting tunnels
inferred← citeshttps://doi.org/10.1016/j.tust.2025.106871openalex-snapshot
LiDAR-inertial coupled simultaneous localization and mapping for deformation monitoring of shield tunnel under construction
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An integrated LiDAR-Inertial-Visual framework for tunnel digital twins under construction: 3D reconstruction, centerline sampling, and point cloud semantic segmentation
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Generating a defect-enriched geometric digital twin for tunnel operation and maintenance using a bottom-up unsupervised approach
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Two-stage instance segmentation method based on Tunnel-SPVNet for shield tunnel segments
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EDA-TCNet: A dual-attention enhanced network for precise point cloud segmentation in tunnel construction
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Group-aware and position-interactive learning for point cloud robust segmentation in subterranean environments
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A Few‐Shot Learning‐Based Point Cloud Semantic Segmentation Network for Tunnel Lining Inspection
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Multi-Class 3D Tunnel Point Cloud Segmentation Using a Deep Learning Method
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Deep Learning–Based Few-Shot Method for Detection and Evaluation of Tunnel Overbreak and Underbreak
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Parallel Dual-Level Attention-Enhanced Semantic Segmentation of 3D Point Clouds in Tunnel Construction
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A Method for Deformation Detection and Reconstruction of Shield Tunnel Based on Point Cloud
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Extraction and location of subway shield tunnel segment joints from RMLS point clouds
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Application of artificial intelligence technology in 3D concrete printing quality inspection and control: a state-of-the-art review
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Optimization of LiDAR scanning positions in tunnel environments: analyzing the impact of station height and lateral deviation
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Advanced Deep Learning Applications for Point Cloud Data in Construction Processes
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A Point Cloud-Based Multiscale Fusion Segmentation Model for Surface Pore Structures in 3-D Printed Concrete
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TUC-Net: A Point Cloud Segmentation Network Based on Neighborhood Feature Perception Aggregation for Tunnels Under Construction
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Spatial‐Relation‐Guided Intelligent Understanding of Complex Railway Tunnel Structures Using Mobile Laser Scanning Data
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Vector Attention-Based Point Cloud Network for Semantic Segmentation Based on Sewer Sonar Point Clouds
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