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<p>The explosive development of artificial intelligence technology greatly promotes the intelligent construction of museums and strengthens their cultural inheritance role. This paper takes artificial intelligence technology as its guide, analyzes the advantages of AI technology in the intelligent museum, and establishes the specific structure of an intelligent museum by combining VR technology. Wisdom Museum can realize the effective repair and presentation of cultural relics data. This paper uses three-dimensional laser scanning technology to obtain cultural relics point cloud data, repair the holes in the point cloud data through RBF, optimize the point cloud data by combining texture mapping, and input the optimized data into the GC-GAN model to realize the three-dimensional digital repair of cultural relics images. Moreover, the smart museum’s intelligent interaction system is built by combining virtual reality technology and related equipment. Taking Dunhuang mural point cloud data as an example, the number of point clouds of the RBF hole repair algorithm differs from the original data by only 5.86%, and the SSIM and PSNR values on the repaired portraits are 0.83 and 26.36 dB, respectively. The SUS total score for the user on the intelligent interactive system is 73.875, and the UEQ factor scores are averaged out at 1-3 points. The creation of a smart museum can be realized using AI technology, providing users with an immersive cultural relics viewing experience and activating the vitality of all kinds of cultural relics in the museum.</p>
</abstract>

The explosive development of artificial intelligence technology greatly promotes the intelligent construction of museums and strengthens their cultural inheritance role. This paper takes artificial intelligence technology as its guide, analyzes the advantages of AI technology in the intelligent museum, and establishes the specific structure of an intelligent museum by combining VR technology. Wisdom Museum can realize the effective repair and presentation of cultural relics data. This paper uses three-dimensional laser scanning technology to obtain cultural relics point cloud data, repair the holes in the point cloud data through RBF, optimize the point cloud data by combining texture mapping, and input the optimized data into the GC-GAN model to realize the three-dimensional digital repair of cultural relics images. Moreover, the smart museum’s intelligent interaction system is built by combining virtual reality technology and related equipment. Taking Dunhuang mural point cloud data as an example, the number of point clouds of the RBF hole repair algorithm differs from the original data by only 5.86%, and the SSIM and PSNR values on the repaired portraits are 0.83 and 26.36 dB, respectively. The SUS total score for the user on the intelligent interactive system is 73.875, and the UEQ factor scores are averaged out at 1-3 points. The creation of a smart museum can be realized using AI technology, providing users with an immersive cultural relics viewing experience and activating the vitality of all kinds of cultural relics in the museum.

Opportunities and Challenges of Artificial Intelligence + Enabling Museum Building

Shanghai Lixin University of Accounting and Finance

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

The explosive development of artificial intelligence technology greatly promotes the intelligent construction of museums and strengthens their cultural...

Opportunities and Challenges of Artificial Intelligence + Enabling Museum Building

Online veröffentlicht: 05. Aug. 2024

Seitenbereich: -

Eingereicht: 25. Apr. 2024

Akzeptiert: 10. Juli 2024

DOI: https://doi.org/10.2478/amns-2024-2093

Schlüsselwörter
AI technology, RBF, Texture mapping, GC-GAN model, Smart museums

© 2024 Zheng Li, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Opportunities and Challenges of Artificial Intelligence + Enabling Museum Building

Online veröffentlicht: 05. Aug. 2024

Seitenbereich: -

Eingereicht: 25. Apr. 2024

Akzeptiert: 10. Juli 2024

DOI: https://doi.org/10.2478/amns-2024-2093

SchlüsselwörterAI technology, RBF, Texture mapping, GC-GAN model, Smart museums

© 2024 Zheng Li, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
AI technology, RBF, Texture mapping, GC-GAN model, Smart museums