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...
| Main Author: | |
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| Format: | Thesis |
| Published: |
Curtin University
2024
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| Online Access: | http://hdl.handle.net/20.500.11937/96628 |
| _version_ | 1848766183282573312 |
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| author | Su, Yang |
| author_facet | Su, Yang |
| author_sort | Su, Yang |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T11:47:05Z |
| format | Thesis |
| id | curtin-20.500.11937-96628 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:47:05Z |
| publishDate | 2024 |
| publisher | Curtin University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-966282024-12-20T01:43:44Z Automatic 3D Reconstruction for As-built Underground Utilities Su, Yang 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. 2024 Thesis http://hdl.handle.net/20.500.11937/96628 Curtin University fulltext |
| spellingShingle | Su, Yang Automatic 3D Reconstruction for As-built Underground Utilities |
| title | Automatic 3D Reconstruction for As-built Underground Utilities |
| title_full | Automatic 3D Reconstruction for As-built Underground Utilities |
| title_fullStr | Automatic 3D Reconstruction for As-built Underground Utilities |
| title_full_unstemmed | Automatic 3D Reconstruction for As-built Underground Utilities |
| title_short | Automatic 3D Reconstruction for As-built Underground Utilities |
| title_sort | automatic 3d reconstruction for as-built underground utilities |
| url | http://hdl.handle.net/20.500.11937/96628 |