3D Firework Reconstruction from a Given Videos

Zhihong Wang; Linyi Hu

doi:doi:10.11648/j.ijics.20180302.13

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3D Firework Reconstruction from a Given Videos

Zhihong Wang, Linyi Hu

Published in International Journal of Information and Communication Sciences (Volume 3, Issue 2)

Received: 16 September 2018 Published: 18 September 2018

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Abstract

Reconstruction of a 3-dimension(3D) firework show from a given videos is a key technology in light source simulation in computer graphics, which can be more effective and real than traditional method. Although the firework model is already very mature, however, to our best knowledge, there is not any existing method that can reconstruct a firework show from a given video. And due to the lack of camera arguments and depth message, reconstruction is very challenging. In this paper, a method is proposed to solve the problem. A rendering model which requires some parameters which describe the color and position information of firework as input and generates a 3D firework show as output is constructed, and then the problem becomes getting the parameters needed for the rendering model from the given video. The parameters are divided into two groups according to the relevance, and then different neural networks including 3D Convolution Neural Network (3D-CNN) and Recurrent Neural Network(RNN) are designed respectively to extract these parameters needed by our rendering model from a given video. It is found to be practicable and effective to reconstruct a 3D firework from a given video by testing this work with some firework videos in various perspective.

Published in	International Journal of Information and Communication Sciences (Volume 3, Issue 2)
DOI	10.11648/j.ijics.20180302.13
Page(s)	33-41
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2018. Published by Science Publishing Group

Keywords

3D-Reconstruction, Neural Networks, Firework

References

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APA Style

Zhihong Wang, Linyi Hu. (2018). 3D Firework Reconstruction from a Given Videos. International Journal of Information and Communication Sciences, 3(2), 33-41. https://doi.org/10.11648/j.ijics.20180302.13

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ACS Style

Zhihong Wang; Linyi Hu. 3D Firework Reconstruction from a Given Videos. Int. J. Inf. Commun. Sci. 2018, 3(2), 33-41. doi: 10.11648/j.ijics.20180302.13

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AMA Style

Zhihong Wang, Linyi Hu. 3D Firework Reconstruction from a Given Videos. Int J Inf Commun Sci. 2018;3(2):33-41. doi: 10.11648/j.ijics.20180302.13

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@article{10.11648/j.ijics.20180302.13,
  author = {Zhihong Wang and Linyi Hu},
  title = {3D Firework Reconstruction from a Given Videos},
  journal = {International Journal of Information and Communication Sciences},
  volume = {3},
  number = {2},
  pages = {33-41},
  doi = {10.11648/j.ijics.20180302.13},
  url = {https://doi.org/10.11648/j.ijics.20180302.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijics.20180302.13},
  abstract = {Reconstruction of a 3-dimension(3D) firework show from a given videos is a key technology in light source simulation in computer graphics, which can be more effective and real than traditional method. Although the firework model is already very mature, however, to our best knowledge, there is not any existing method that can reconstruct a firework show from a given video. And due to the lack of camera arguments and depth message, reconstruction is very challenging. In this paper, a method is proposed to solve the problem. A rendering model which requires some parameters which describe the color and position information of firework as input and generates a 3D firework show as output is constructed, and then the problem becomes getting the parameters needed for the rendering model from the given video. The parameters are divided into two groups according to the relevance, and then different neural networks including 3D Convolution Neural Network (3D-CNN) and Recurrent Neural Network(RNN) are designed respectively to extract these parameters needed by our rendering model from a given video. It is found to be practicable and effective to reconstruct a 3D firework from a given video by testing this work with some firework videos in various perspective.},
 year = {2018}
}

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TY  - JOUR
T1  - 3D Firework Reconstruction from a Given Videos
AU  - Zhihong Wang
AU  - Linyi Hu
Y1  - 2018/09/18
PY  - 2018
N1  - https://doi.org/10.11648/j.ijics.20180302.13
DO  - 10.11648/j.ijics.20180302.13
T2  - International Journal of Information and Communication Sciences
JF  - International Journal of Information and Communication Sciences
JO  - International Journal of Information and Communication Sciences
SP  - 33
EP  - 41
PB  - Science Publishing Group
SN  - 2575-1719
UR  - https://doi.org/10.11648/j.ijics.20180302.13
AB  - Reconstruction of a 3-dimension(3D) firework show from a given videos is a key technology in light source simulation in computer graphics, which can be more effective and real than traditional method. Although the firework model is already very mature, however, to our best knowledge, there is not any existing method that can reconstruct a firework show from a given video. And due to the lack of camera arguments and depth message, reconstruction is very challenging. In this paper, a method is proposed to solve the problem. A rendering model which requires some parameters which describe the color and position information of firework as input and generates a 3D firework show as output is constructed, and then the problem becomes getting the parameters needed for the rendering model from the given video. The parameters are divided into two groups according to the relevance, and then different neural networks including 3D Convolution Neural Network (3D-CNN) and Recurrent Neural Network(RNN) are designed respectively to extract these parameters needed by our rendering model from a given video. It is found to be practicable and effective to reconstruct a 3D firework from a given video by testing this work with some firework videos in various perspective.
VL  - 3
IS  - 2
ER  -

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Author Information

Zhihong Wang

State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and Engineering, Beihang University, Beijing, China
Linyi Hu

State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and Engineering, Beihang University, Beijing, China

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APA Style

Zhihong Wang, Linyi Hu. (2018). 3D Firework Reconstruction from a Given Videos. International Journal of Information and Communication Sciences, 3(2), 33-41. https://doi.org/10.11648/j.ijics.20180302.13

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ACS Style

Zhihong Wang; Linyi Hu. 3D Firework Reconstruction from a Given Videos. Int. J. Inf. Commun. Sci. 2018, 3(2), 33-41. doi: 10.11648/j.ijics.20180302.13

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AMA Style

Zhihong Wang, Linyi Hu. 3D Firework Reconstruction from a Given Videos. Int J Inf Commun Sci. 2018;3(2):33-41. doi: 10.11648/j.ijics.20180302.13

Copy | Download

@article{10.11648/j.ijics.20180302.13,
  author = {Zhihong Wang and Linyi Hu},
  title = {3D Firework Reconstruction from a Given Videos},
  journal = {International Journal of Information and Communication Sciences},
  volume = {3},
  number = {2},
  pages = {33-41},
  doi = {10.11648/j.ijics.20180302.13},
  url = {https://doi.org/10.11648/j.ijics.20180302.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijics.20180302.13},
  abstract = {Reconstruction of a 3-dimension(3D) firework show from a given videos is a key technology in light source simulation in computer graphics, which can be more effective and real than traditional method. Although the firework model is already very mature, however, to our best knowledge, there is not any existing method that can reconstruct a firework show from a given video. And due to the lack of camera arguments and depth message, reconstruction is very challenging. In this paper, a method is proposed to solve the problem. A rendering model which requires some parameters which describe the color and position information of firework as input and generates a 3D firework show as output is constructed, and then the problem becomes getting the parameters needed for the rendering model from the given video. The parameters are divided into two groups according to the relevance, and then different neural networks including 3D Convolution Neural Network (3D-CNN) and Recurrent Neural Network(RNN) are designed respectively to extract these parameters needed by our rendering model from a given video. It is found to be practicable and effective to reconstruct a 3D firework from a given video by testing this work with some firework videos in various perspective.},
 year = {2018}
}

Copy | Download

TY  - JOUR
T1  - 3D Firework Reconstruction from a Given Videos
AU  - Zhihong Wang
AU  - Linyi Hu
Y1  - 2018/09/18
PY  - 2018
N1  - https://doi.org/10.11648/j.ijics.20180302.13
DO  - 10.11648/j.ijics.20180302.13
T2  - International Journal of Information and Communication Sciences
JF  - International Journal of Information and Communication Sciences
JO  - International Journal of Information and Communication Sciences
SP  - 33
EP  - 41
PB  - Science Publishing Group
SN  - 2575-1719
UR  - https://doi.org/10.11648/j.ijics.20180302.13
AB  - Reconstruction of a 3-dimension(3D) firework show from a given videos is a key technology in light source simulation in computer graphics, which can be more effective and real than traditional method. Although the firework model is already very mature, however, to our best knowledge, there is not any existing method that can reconstruct a firework show from a given video. And due to the lack of camera arguments and depth message, reconstruction is very challenging. In this paper, a method is proposed to solve the problem. A rendering model which requires some parameters which describe the color and position information of firework as input and generates a 3D firework show as output is constructed, and then the problem becomes getting the parameters needed for the rendering model from the given video. The parameters are divided into two groups according to the relevance, and then different neural networks including 3D Convolution Neural Network (3D-CNN) and Recurrent Neural Network(RNN) are designed respectively to extract these parameters needed by our rendering model from a given video. It is found to be practicable and effective to reconstruct a 3D firework from a given video by testing this work with some firework videos in various perspective.
VL  - 3
IS  - 2
ER  -

Copy | Download