Overcoming single-technology limitations in digital heritage preservation: A study of the LiPhoScan 3D reconstruction model
With the increasing demand for the digital preservation of cultural heritage, high-precision 3D reconstruction of museum artifacts has become an important research direction. However, single technology approaches face limitations in practical applications, such as insufficient capture of geometric d...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-04-01
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| Series: | Alexandria Engineering Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016824016934 |
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| Summary: | With the increasing demand for the digital preservation of cultural heritage, high-precision 3D reconstruction of museum artifacts has become an important research direction. However, single technology approaches face limitations in practical applications, such as insufficient capture of geometric details, poor texture fidelity, and suboptimal geometric accuracy. To address these issues, this paper proposes the LiPhoScan hybrid 3D reconstruction model, which integrates LiDAR, photogrammetry, and structured light scanning technologies. This model leverages the high geometric accuracy of LiDAR, the high texture fidelity of photogrammetry, and the detail-capturing ability of structured light scanning to overcome the limitations of individual technologies, providing a more comprehensive and refined 3D reconstruction solution. Experimental results show that LiPhoScan improves geometric accuracy by 15% and texture fidelity by 20% compared to traditional methods. In addition, compared to existing single-technology approaches, LiPhoScan demonstrates significant advantages in detail fidelity and overall geometric consistency. This offers an innovative solution for the digital preservation of museum artifacts and lays a solid foundation for high-precision and high-detail 3D reconstruction tasks. Future research will incorporate parallel computing and efficient data processing methods to further enhance the model’s computational efficiency. |
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| ISSN: | 1110-0168 |