Sewer defect detection from 3D point clouds using a transformer-based deep learning model
Targeting the defect classification from 3D point clouds, this research develops a deep
learning method named the Transformer-based point cloud classification network …
learning method named the Transformer-based point cloud classification network …
UnrollingNet: An attention-based deep learning approach for the segmentation of large-scale point clouds of tunnels
A novel projection-based learning method named UnrollingNet is developed to conduct a
multi-label segmentation of various objects including seepage from 3D point clouds of …
multi-label segmentation of various objects including seepage from 3D point clouds of …
Sampling-attention deep learning network with transfer learning for large-scale urban point cloud semantic segmentation
Targeting the development of smart cities to facilitate the significant analysis of large-scale
urban for construction and update. This research develops a new method named transfer …
urban for construction and update. This research develops a new method named transfer …
A novel extended multimodal AI framework towards vulnerability detection in smart contracts
W Jie, Q Chen, J Wang, ASV Koe, J Li, P Huang… - Information …, 2023 - Elsevier
Current automatic data-driven vulnerability detection in smart contracts selects and
processes features of interest under black box settings without empirical justification. In this …
processes features of interest under black box settings without empirical justification. In this …
Semi-supervised learning-based point cloud network for segmentation of 3D tunnel scenes
Automatic identifying target multi-class objects in tunnel scenes from 3D point clouds is
widely thought to be critical for maintaining the healthy condition of the tunnel using deep …
widely thought to be critical for maintaining the healthy condition of the tunnel using deep …
Segmentation of point clouds via joint semantic and geometric features for 3D modeling of the built environment
Generating 3D models from point cloud data is a common Virtual Design and Construction
(VDC) service. Research has focused on automating several key steps, including …
(VDC) service. Research has focused on automating several key steps, including …
Continuous conditional random field convolution for point cloud segmentation
Point cloud segmentation is the foundation of 3D environmental perception for modern
intelligent systems. To solve this problem and image segmentation, conditional random …
intelligent systems. To solve this problem and image segmentation, conditional random …
An encoder-decoder deep learning method for multi-class object segmentation from 3D tunnel point clouds
A Ji, AWZ Chew, X Xue, L Zhang - Automation in Construction, 2022 - Elsevier
Discovering seepage is widely thought to be critical for maintaining the healthy conditions of
the tunnel. Unfortunately, most of the seepage surveys are still manual with tedious, time …
the tunnel. Unfortunately, most of the seepage surveys are still manual with tedious, time …
Attention-enhanced sampling point cloud network (ASPCNet) for efficient 3D tunnel semantic segmentation
Laser scanning is used as a modern means to capture data from tunnels to assess their
condition, but automated processing requires robust component detection and deterioration …
condition, but automated processing requires robust component detection and deterioration …
Local and global structure for urban ALS point cloud semantic segmentation with ground-aware attention
Interpretation of airborne laser scanning (ALS) point clouds plays a notable role in
geoinformation production. As a critical step for interpretation, accurate semantic …
geoinformation production. As a critical step for interpretation, accurate semantic …