相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。An encoder-decoder deep learning method for multi-class object segmentation from 3D tunnel point clouds
Ankang Ji et al.
AUTOMATION IN CONSTRUCTION (2022)
Sewer defect detection from 3D point clouds using a transformer-based deep learning model
Yunxiang Zhou et al.
AUTOMATION IN CONSTRUCTION (2022)
Multi-objective optimization for improved project management: Current status and future directions
Kai Guo et al.
AUTOMATION IN CONSTRUCTION (2022)
UnrollingNet: An attention-based deep learning approach for the segmentation of large-scale point clouds of tunnels
Zhaoxiang Zhang et al.
AUTOMATION IN CONSTRUCTION (2022)
Automatic defect detection of metro tunnel surfaces using a vision-based inspection system
Dawei Li et al.
ADVANCED ENGINEERING INFORMATICS (2021)
A deep learning-based approach for refined crack evaluation from shield tunnel lining images
Shuai Zhao et al.
AUTOMATION IN CONSTRUCTION (2021)
Automated semantic segmentation of industrial point clouds using ResPointNet plus
Chao Yin et al.
AUTOMATION IN CONSTRUCTION (2021)
Semantic segmentation of bridge components based on hierarchical point cloud model
Jun S. Lee et al.
AUTOMATION IN CONSTRUCTION (2021)
PointVGG: Graph convolutional network with progressive aggregating features on point clouds
Rongkang Li et al.
NEUROCOMPUTING (2021)
A Heuristic Sampling Method for Maintaining the Probability Distribution
Jiao-Yun Yang et al.
JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY (2021)
PCT: Point cloud transformer
Meng-Hao Guo et al.
COMPUTATIONAL VISUAL MEDIA (2021)
Image-based concrete crack detection in tunnels using deep fully convolutional networks
Yupeng Ren et al.
CONSTRUCTION AND BUILDING MATERIALS (2020)
Deep learning-based image instance segmentation for moisture marks of shield tunnel lining
Shuai Zhao et al.
TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY (2020)
Point Cloud Semantic Segmentation Using a Deep Learning Framework for Cultural Heritage
Roberto Pierdicca et al.
REMOTE SENSING (2020)
Point attention network for semantic segmentation of 3D point clouds
Mingtao Feng et al.
PATTERN RECOGNITION (2020)
Feature fusion network based on attention mechanism for 3D semantic segmentation of point clouds
Heng Zhou et al.
PATTERN RECOGNITION LETTERS (2020)
Attention-based relation and context modeling for point cloud semantic segmentation
Zhiyu Hu et al.
COMPUTERS & GRAPHICS-UK (2020)
Multi-view semantic learning network for point cloud based 3D object detection
Yongguang Yang et al.
NEUROCOMPUTING (2020)
Point cloud semantic scene segmentation based on coordinate convolution
Zhaoxuan Zhang et al.
COMPUTER ANIMATION AND VIRTUAL WORLDS (2020)
Factors in the development of urban underground space surrounding metro stations: A case study of Osaka, Japan
Jian Peng et al.
TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY (2019)
A Review of Deep Learning-Based Semantic Segmentation for Point Cloud
Jiaying Zhang et al.
IEEE ACCESS (2019)
JUSTLOOKUP: ONE MILLISECOND DEEP FEATURE EXTRACTION FOR POINT CLOUDS BY LOOKUP TABLES
Hongxin Lin et al.
2019 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO (ICME) (2019)
Damage detection and quantitative analysis of shield tunnel structure
Zhen Huang et al.
AUTOMATION IN CONSTRUCTION (2018)
Sensitivity analysis of structural health risk in operational tunnels
Wenli Liu et al.
AUTOMATION IN CONSTRUCTION (2018)
Weighted Focal Loss: An Effective Loss Function to Overcome Unbalance Problem of Chest X-ray14
Ruoxi Qin et al.
3RD INTERNATIONAL CONFERENCE ON AUTOMATION, CONTROL AND ROBOTICS ENGINEERING (CACRE 2018) (2018)
Transportation infrastructure impacts on firm location: the effect of a new metro line in the suburbs of Madrid
Lucia Mejia-Dorantes et al.
JOURNAL OF TRANSPORT GEOGRAPHY (2012)