Volume 3, Issue 2, June 2018, Page: 33-41
3D Firework Reconstruction from a Given Videos
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
Received: Sep. 16, 2018;       Published: Sep. 18, 2018
DOI: 10.11648/j.ijics.20180302.13      View  384      Downloads  19
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.
Keywords
3D-Reconstruction, Neural Networks, Firework
To cite this article
Zhihong Wang, Linyi Hu, 3D Firework Reconstruction from a Given Videos, International Journal of Information and Communication Sciences. Vol. 3, No. 2, 2018, pp. 33-41. doi: 10.11648/j.ijics.20180302.13
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