Automatic 3D Reconstruction for As-built Underground Utilities
This thesis addresses three challenges in the 3D reconstruction of underground utilities. Firstly, it introduces a high-precision ground penetrating radar-based deep learning model to enhance localization accuracy. Secondly, it presents an unsupervised deep learning model for effective 3D reconstruc...
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| Format: | Thesis |
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Curtin University
2024
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| Online Access: | http://hdl.handle.net/20.500.11937/96628 |
| Summary: | This thesis addresses three challenges in the 3D reconstruction of underground utilities. Firstly, it introduces a high-precision ground penetrating radar-based deep learning model to enhance localization accuracy. Secondly, it presents an unsupervised deep learning model for effective 3D reconstruction under low-light conditions. Finally, it proposes a graph convolutional network-based model to accurately complete missing topological data of utility networks. Experimental results demonstrate significant improvements in accuracy, speed, and data integrity. |
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