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Depth Image Super Resolution for 3D Reconstruction of Oil Reflnery Buildings

  • Shuaihao Li
  • Bin Zhang
  • Xinfeng Yang
  • Yanxiang He
  • Min ChenEmail author
Article
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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.

Keywords

depth image 3D reconstruction super resolution progressive convolution neural network oil refinery 

Notes

Acknowledgement

This study was funded by the Nature Science Foundation of China (Grant 61373039), China National Mapping and Geographic Information Bureau Engineering Technology Research Center (ID: SID770170101) and Research Center for Culture-Technology Integration Innovation, Key Research Base of Humanities and Social Science of Province, China (ID: WK201704).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Shuaihao Li
    • 1
  • Bin Zhang
    • 2
    • 3
  • Xinfeng Yang
    • 1
  • Yanxiang He
    • 1
  • Min Chen
    • 1
    Email author
  1. 1.School of Computer ScienceWuhan UniversityWuhanChina
  2. 2.School of Remote Sensing and Information EngineeringWuhan UniversityWuhanChina
  3. 3.Department of Computer ScienceCity University of Hong KongKowloon TongChina

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