Multi-Class 3D Tunnel Point Cloud Segmentation Using a Deep Learning Method
Publication: Computing in Civil Engineering 2023
ABSTRACT
A deep learning method is proposed to act on point clouds for segmentation, which can feed the data into a built network based on an encoder-decoder architecture coupled with an improved 3D dual attention module to extract and learn features. To verify the effectiveness and feasibility of the proposed model, a tunnel point cloud dataset collected in a metro tunnel project is used. The experimental results show that the proposed model has a plausible performance with an Intersection over Union (MIoU) of 0.8597, and it outperforms other state-of-the-art methods such as PointNet and DGCNN. Overall, the proposed model shows excellent performance and provides effective and accurate results for multi-class segmentation on 3D tunnel point clouds.
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Information & Authors
Information
Published In
Computing in Civil Engineering 2023
Pages: 926 - 934
History
Published online: Jan 25, 2024
ASCE Technical Topics:
- Architectural engineering
- Architecture
- Artificial intelligence and machine learning
- Computer networks
- Computer programming
- Computing in civil engineering
- Coupling
- Data collection
- Engineering fundamentals
- Geotechnical engineering
- Methodology (by type)
- Model accuracy
- Models (by type)
- Neural networks
- Research methods (by type)
- Structural engineering
- Structural members
- Structural systems
- Three-dimensional models
- Tunnels
Authors
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