Depth Image Super Resolution for 3D Reconstruction of Oil Reflnery Buildings
- Autores: Li S.1, Zhang B.2,3, Yang X.1, He Y.1, Chen M.1
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Afiliações:
- School of Computer Science, Wuhan University
- School of Remote Sensing and Information Engineering, Wuhan University
- Department of Computer Science, City University of Hong Kong
- Edição: Volume 55, Nº 4 (2019)
- Páginas: 491-496
- Seção: Article
- URL: https://journals.rcsi.science/0009-3092/article/view/235947
- DOI: https://doi.org/10.1007/s10553-019-01055-z
- ID: 235947
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Resumo
Time-of-Flight (ToF) camera can collect the depth data of dynamic scene surface in real time, which has been applied to 3D reconstruction of refinery buildings. However; due to the limitations of sensor hardware, the resolution of the depth image obtained is very low, so it cannot meet the requirements of dense depth of scene in practical applications such as 3D reconstruction. Therefore, it is necessary to make a breakthrough in software and design a good algorithm to improve the resolution of depth image. We propose of an algorithm of depth image super-resolution by using fusion of multiple progressive convolution neural networks, which uses a context-based network fusion framework to fuse multiple different progressive networks, so as to improve individual network performance and efficiency while maintaining the simplicity of network training. Finally, we have carried out experiments on the public data set, and the experimental results show that the proposed algorithm has reached or even exceeded the most advanced algorithms at present.
Sobre autores
Shuaihao Li
School of Computer Science, Wuhan University
Email: chenmin@whu.edu.cn
República Popular da China, Wuhan
Bin Zhang
School of Remote Sensing and Information Engineering, Wuhan University; Department of Computer Science, City University of Hong Kong
Email: chenmin@whu.edu.cn
República Popular da China, Wuhan; Kowloon Tong
Xinfeng Yang
School of Computer Science, Wuhan University
Email: chenmin@whu.edu.cn
República Popular da China, Wuhan
Yanxiang He
School of Computer Science, Wuhan University
Email: chenmin@whu.edu.cn
República Popular da China, Wuhan
Min Chen
School of Computer Science, Wuhan University
Autor responsável pela correspondência
Email: chenmin@whu.edu.cn
República Popular da China, Wuhan