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Research on key technology of virtual restoration of ancient buildings based on convolutional neural network
Virtual restoration of ancient buildings has been paid more and more attention in the field of protecting and studying ancient architectural cultural heritage. Using various virtual restoration related technologies to establish a realistic virtual environment is a current research hotspot. It complements the modeling and rendering of three-dimensional geometric model based on real scene measurement data through the splicing and fusion of photos and three-dimensional reconstruction process, and partially solves the difficulty of measuring damaged or even non-existent ancient buildings, it is an efficient method to realize the virtual restoration of ancient buildings. Among them, image restoration is also a very important thing, which has great practical significance. Image restoration technology is a hot research direction at present. Using the advantages of convolutional neural network model in image restoration, two hot issues of image deblurring and image super-resolution reconstruction involved in image restoration are studied. Virtual restoration technology has been well developed in science and technology, medical treatment, military and other fields, and at the same time, it has promoted the construction of virtual ancient architecture system. The virtual restoration of ancient architectural cultural heritage is the product of the combination of science and art. The use of virtual restoration technology can digitize the ancient architectural cultural heritage. The virtual ancient architectural system breaks the restrictions of time and space, improves the exhibition effect and exhibition efficiency of ancient architectural cultural heritage, and plays a certain supporting role in the application of protecting cultural heritage.
Research on key technology of virtual restoration of ancient buildings based on convolutional neural network
Virtual restoration of ancient buildings has been paid more and more attention in the field of protecting and studying ancient architectural cultural heritage. Using various virtual restoration related technologies to establish a realistic virtual environment is a current research hotspot. It complements the modeling and rendering of three-dimensional geometric model based on real scene measurement data through the splicing and fusion of photos and three-dimensional reconstruction process, and partially solves the difficulty of measuring damaged or even non-existent ancient buildings, it is an efficient method to realize the virtual restoration of ancient buildings. Among them, image restoration is also a very important thing, which has great practical significance. Image restoration technology is a hot research direction at present. Using the advantages of convolutional neural network model in image restoration, two hot issues of image deblurring and image super-resolution reconstruction involved in image restoration are studied. Virtual restoration technology has been well developed in science and technology, medical treatment, military and other fields, and at the same time, it has promoted the construction of virtual ancient architecture system. The virtual restoration of ancient architectural cultural heritage is the product of the combination of science and art. The use of virtual restoration technology can digitize the ancient architectural cultural heritage. The virtual ancient architectural system breaks the restrictions of time and space, improves the exhibition effect and exhibition efficiency of ancient architectural cultural heritage, and plays a certain supporting role in the application of protecting cultural heritage.
Research on key technology of virtual restoration of ancient buildings based on convolutional neural network
Zhou, Xiaoping (author) / Liu, Yanmin (author) / Lian, Xiaobo (author) / Guo, Huiting (author) / Lee, Sangyoung (author)
2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022) ; 2022 ; Zhuhai,China
Proc. SPIE ; 12348
2022-11-10
Conference paper
Electronic Resource
English
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