Abstract
The demand for 3D video content is growing. Conventional 3D video creation approaches need certain devices to take the 3D videos or lots of people to do the labor-intensive depth labeling work. To reduce the manpower and time consumption, many automatic approaches has been developed to convert legacy 2D videos into 3D. However, due to the strict quality requirements in video production industry, most of the automatic conversion methods are suffered from many quality issues and could not be used in the actual production. As a result manual or semi-automatic 3D video generation approaches are still mainstream 3D video generation technologies. In our project, we took advantage of an automatic video generation method and tried to apply human-computer interactions in its process procedure [1] in the aim to find a balance between time efficiency and depth map generation quality. The novelty of the paper relies on the successful attempt on improving an automatic 3D video generation method in the angle of video and film industry.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Karsch, K., Liu, C., Kang, S.B.: Depth transfer: depth extraction from video using non-parametric sampling. IEEE Trans. Pattern Anal. Mach. Intell. 36(11), 2144–2158 (2014)
https://en.wikipedia.org/wiki/stereoscopy. Accessed 6 May 2016
http://www.usnews.com/news/articles/2015/09/24/samsung-oculus-make-virtual-reality-affordable. Accessed 4 May 2016
Zhang, L., Tam, W.J.: Stereoscopic image generation based on depth images for 3D TV. IEEE Trans. Broadcast. 51(2), 191–199 (2005)
Liu, B., Gould, S., Koller, D.: Single image depth estimation from predicted semantic labels. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1253–1260. IEEE (2010)
Konrad, J., Wang, M., Ishwar, P.: 2D-to-3D image conversion by learning depth from examples (2012)
Saxena, A., Sun, M., Ng, A.Y.: Make3D: learning 3D scene structure from a single still image. IEEE Trans. Pattern Anal. Mach. Intell. 31(5), 824–840 (2009)
Eigen, D., Puhrsch, C., Fergus, R.: Depth map prediction from a single image using a multi-scale deep network. In: Advances in Neural Information Processing Systems, pp. 2366–2374 (2014)
Oliva, A., Torralba, A.: Modeling the shape of the scene: a holistic representation of the spatial envelope. Int. J. Comput. Vis. 42(3), 145–175 (2001)
Liu, C.: Beyond pixels: exploring new representations and applications for motion analysis. Ph.D. dissertation. Citeseer (2009)
Liu, C., Yuen, J., Torralba, A., Sivic, J., Freeman, W.T.: SIFT flow: dense correspondence across different scenes. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008. LNCS, vol. 5304, pp. 28–42. Springer, Heidelberg (2008). doi:10.1007/978-3-540-88690-7_3
Karsch, K., Liu, C., Kang, S.B.: Depth extraction from video using non-parametric sampling (2012)
Pietikainen, M., Heikkila, M.: A texture-based method for modeling the back-ground and detecting moving objects. IEEE Trans. Pattern Anal. Mach. Intell. 28(4), 657–662 (2006)
Deschamps, A., Howe, N.R.: Better foreground segmentation through graph cuts (2004)
Behnke, S., Stuckler, J.: Efficient dense rigid-body motion segmentation and estimation in RGBD video. Int. J. Comput. Vis. 113(3), 233–245 (2015)
http://research.microsoft.com/en-us/downloads/29d28301-1079-4435-9810-74709376bce1/. Accessed 20 May 2016
Acknowledgment
This work was supported in part by the National Key Technology Research and Development Program of the Ministry of Science and Technology of China (No. 2014BAK14B01), Natural Science Foundation of China (No. 61379075, No. 61472362), Science and Technology Plan Project of Zhejiang Province (No. 2014C33070), Zhejiang Provincial public welfare technology research on Society development (No. 2015C33081). Zhoushan Municipal Science and Technology Plan Project.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer-Verlag GmbH Germany
About this chapter
Cite this chapter
Yang, T., Wang, X., Wang, H., Li, X. (2017). Depth Map Enhancement with Interaction in 2D-to-3D Video Conversion. In: Pan, Z., Cheok, A., Müller, W., Zhang, M. (eds) Transactions on Edutainment XIII. Lecture Notes in Computer Science(), vol 10092. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-54395-5_16
Download citation
DOI: https://doi.org/10.1007/978-3-662-54395-5_16
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-54394-8
Online ISBN: 978-3-662-54395-5
eBook Packages: Computer ScienceComputer Science (R0)