Advertisement

Walking into Panoramic and Immersive 3D Video

  • Yingbin Nie
  • Jianmin JiangEmail author
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 188)

Abstract

To enable viewers to perceive the video content as if he or she is walking inside the video scenes, we need to have two essential video technologies. One is to present the audience with panoramic videos with 360°, and the other is view-adaptive video playback, i.e. presenting video scenes in accordance with the change of viewing angles. For the first technology, we propose a fast video stitching algorithm via exploiting the audio information for frame synchronization, and for the second, we propose a Fake-3D and True-3D mix method to immerse viewers inside the video scene via its dynamic playback of panoramic videos, adaptive to multi-view changes. Our proposed technologies have great potential in practical applications, such as virtual reality gaming and new concept movie shows etc.

Keywords

Panoramic 3D videos Free-view video processing Dynamic video playback Video stitching Fake-3D True-3D Virtual reality 

References

  1. 1.
    McVicar, M., Santos-Rodríguez, R., Ni, Y., De Bie, T.: Automatic chord estimation from audio: a review of the state of the art. IEEE Trans. Audio Speech Lang. Process. 22, 556–573 (2014)CrossRefGoogle Scholar
  2. 2.
    Brown, M., Lowe, D.G.: Automatic panoramic image stitching using invariant features. Int. J. Comput. Vis. (2007)Google Scholar
  3. 3.
    Tang, W., Wong, T.T., Heng, P.: A system for real-time panorama generation and display in tele-immersive applications. IEEE Trans. Mult. 7, 280–292 (2005)CrossRefGoogle Scholar
  4. 4.
    Burt, P., Adelson, E.: A multiresolution spline with application to image mosaics. ACM Trans. Graphics 2, 217–236 (1983)CrossRefGoogle Scholar
  5. 5.
    Xu, W., Mulligan, J.: Panoramic video stitching from commodity HDTV cameras. Multimedia Syst. 19, 407–426 (2013)CrossRefGoogle Scholar
  6. 6.
    Molina, E., Zhu, Z.: Persistent aerial video registration and fast multi-view mosaicking. IEEE Trans. Image Process. 23, 2184–2192 (2014)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Song, X., Zhang, J., Han, Y., Jiang, J.: Semi-supervised feature selection via hierarchical regression for web image classification. Multimedia Syst. 22(1), 41–49 (2016)CrossRefGoogle Scholar
  8. 8.
    Zhang, J., Han, Y., Jiang, J.: Tensor rank selection for multimedia analysis. J. Vis. Commun. Image Represent. 30, 376–392 (2015)CrossRefGoogle Scholar
  9. 9.
    Li, J.Y., Jiang, J.: Nonrigid structure from motion via sparse representation. IEEE Trans. Cybern. 45(8), 1401–1413 (2015)CrossRefGoogle Scholar
  10. 10.
    Pan, Z.L., Ming, Z., Zhong, H., Wang, X., Xu, C.: Compressed knowledge transfer via factorization machine for heterogeneous collaborative recommendation. Knowl. Based Syst. 85, 234–244 (2015)CrossRefGoogle Scholar
  11. 11.
    Bano, S., Cavallaro, A.: Discovery and organization of multi-camera user-generated videos of the same event. Inf. Sci. 302, 108–121 (2015)CrossRefGoogle Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2017

Authors and Affiliations

  1. 1.Research Institute for Future Media Computing, School of Computer Science and Software EngineeringShenzhen UniversityShenzhenChina

Personalised recommendations