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3D Meteorological Radar Data Visualization with Point Cloud Completion and Poisson Surface Reconstruction

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Pattern Recognition and Computer Vision (PRCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13536))

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Abstract

3D visualization of meteorological radar data can not only reflect the basic characteristics of atmospheric motion and clouds spatial distribution, but also help forecasters predict weather changes more efficiently and accurately. The cone information obtained by radar has the features of sparse, discrete, uneven and irregular distribution, which will lead roughly drawn in the process of 3D rendering due to less triangular surfaces. In this paper, a surface drawing method including Point Cloud Completion and improved Poisson Surface reconstruction was proposed to implement high quality radar data surface drawing. Firstly, the skills of Point Cloud was introduced to complete irregular and scattered sparse cloud data, mathematical models were established for different types of echo data, and corresponding completion was investigated to fill the missing spatial data. Secondly, the improved Poisson Surface Reconstruction based on bilateral filtering (BPSR) was used to render the 3D surface data after completion to make 3D visualization more realistic. Experiments showed that the proposed method could well complete and plot irregular meteorological radar data, the data were more denser after processing with the Point Cloud Completion, and the BPSR improved the efficiency and fineness and realized higher reconstruction quality.

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Acknowledgement

This work is supported by Program for Innovative Research Team in University of Yunnan Province(IRTSTYN) and Professional Degree Postgraduate Practical Innovation Project of Yunnan University(2021Y164).

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Authors and Affiliations

  1. School of Information Science and Engineering, Yunnan University, Kunming, China

    Xiao Xu & Murong Jiang

Authors
  1. Xiao Xu
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  2. Murong Jiang
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Corresponding author

Correspondence to Murong Jiang .

Editor information

Editors and Affiliations

  1. Southern University of Science and Technology, Shenzhen, China

    Shiqi Yu

  2. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Zhaoxiang Zhang

  3. Hong Kong Baptist University, Hong Kong, China

    Pong C. Yuen

  4. Northwestern Polytechnical University, Xi'an, China

    Junwei Han

  5. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Tieniu Tan

  6. Hong Kong Baptist University, Hong Kong, China

    Yike Guo

  7. Sun Yat-sen University, Guangzhou, China

    Jianhuang Lai

  8. Southern University of Science and Technology, Shenzhen, China

    Jianguo Zhang

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Xu, X., Jiang, M. (2022). 3D Meteorological Radar Data Visualization with Point Cloud Completion and Poisson Surface Reconstruction. In: Yu, S., et al. Pattern Recognition and Computer Vision. PRCV 2022. Lecture Notes in Computer Science, vol 13536. Springer, Cham. https://doi.org/10.1007/978-3-031-18913-5_11

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  • DOI: https://doi.org/10.1007/978-3-031-18913-5_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-18912-8

  • Online ISBN: 978-3-031-18913-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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