Skip to main content

The Overview of 2D to 3D Automatic Conversion

  • Conference paper
  • First Online:
  • 1418 Accesses

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1043))

Abstract

With the rapid development of 3D devices, the depth map extraction method of 2D to 3D conversion has become a research hot spot in the field of computer vision. In this paper, on account of collected literatures and documents, we mainly introduces two methods of automatic depth map extraction based respectively on depth clues and machine learning. The depth map extraction method based on clues of several implementation algorithms is introduced, and its respective advantages and disadvantages are summarized. While for the depth map extraction method based on machine learning, we show the process of depth map extraction as an example. Moreover the parametric method and the non-parametric method are compared, and their respective advantages and disadvantages are pointed out. Finally we summarize the improved depth map extraction algorithm in the recent years, and the technical prospect is also discussed.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Herrera, J.L., Del-Blanco, C.R., García, N.: Automatic depth extraction from 2D images using a cluster-based learning framework. IEEE Trans. Image Process. 319, 3288–3299 (2018)

    Article  MathSciNet  Google Scholar 

  2. Cozman, F., Krotkov, E.: Depth from scattering. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 801–806. IEEE Press, San Juan (1997)

    Google Scholar 

  3. Choe, Y., Kashyap, R.L.: Shape from textured and shaded surface. In: 10th International Conference on Pattern Recognition, pp. 294–296. IEEE Press, Atlantic City (1990)

    Google Scholar 

  4. Battiato, S., Curti, S., Cascia, M.L., Tortora, M., Scordato, E.: Depth map generation by image classification. In: SPIE International Society for Optical Engineering. San Jose, CA, pp. 95–104 (2004)

    Google Scholar 

  5. Han, K., Hong, K.: Geometric and texture cue based depth-map estimation for 2D to 3D image conversion. In: IEEE International Conference on Consumer Electronics, pp. 651–652. IEEE Press, ChiangMai (2011)

    Google Scholar 

  6. Ji, P., Wang, L., Li, D., Zhang, M.: An automatic 2D to 3D conversion algorithm using multi-depth cues. In: Proceedings of International Conference on Audio, Language and Image Processing, pp. 546–550. IEEE Press, Shanghai (2012)

    Google Scholar 

  7. Wafa, A., Nasiopoulos, P., Leung, V.C., Pourazad, M.T.: Automatic real-time 2D-to-3D conversion for scenic views. In: 7th International Workshop on Quality of Multimedia Experience, pp. 1–5. IEEE Press, Costa Navarino (2015)

    Google Scholar 

  8. Levin, A., Lischinski, D., Weiss, Y.: A closed-form solution to natural image matting. IEEE Trans. Pattern Anal. Mach. Intell. 350, 228–242 (2008)

    Article  Google Scholar 

  9. Chang, Y.-L., Chen, W.-Y., Chang, J.-Y., Tsai, Y.-M., Lee, C.-L., Chen, L.-G.: Priority depth fusion for the 2D to 3D conversion system. In: Proceedings of SPIE 3D Image Capture Applications, p. 680513 (2008)

    Google Scholar 

  10. Harman, P.V., Flack, J., Fox, S.: Rapid 2D-to-3D conversion. In: Proceeding of SPIE, pp. 78–86. Society of Photo-Optical Instrumentation Engineers Press, San Jose (2002)

    Google Scholar 

  11. Saxena, A., Chung, S.H., Ng, A.Y.: Learning depth from single monocular images. In: International Conference on Neural Information Processing Systems, Taiwan (2005)

    Google Scholar 

  12. Konrad, J., Wang, M., Ishwar, P.: 2D-to-3D image conversion by learning depth from examples. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 16–22. IEEE Press, Providence (2012)

    Google Scholar 

  13. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 886–893. IEEE Press, San Diego (2005)

    Google Scholar 

  14. Herrera, J.L., del-Blanco, C.R., García, N.: A novel 2D to 3D video conversion system based on a machine learning approach. IEEE Trans. Consum. Electron. 736, 429–436 (2016)

    Article  Google Scholar 

  15. Liu, Y., Lin, X., Zhang, Q., Izquierdo, E.: Improved indoor scene geometry recognition from single image based on depth map. In: Proceedings of Image, Video, and Multidimensional Signal Processing, pp. 1–4. IEEE Press, Seoul (2013)

    Google Scholar 

  16. Yuan, H.X., Wu, S.Q., Yu, H.Q.: Semantic-level depth migration 2D to 3D algorithm. J. Comput.-Aided Des. Comput. Graph. 301, 72–80 (2014)

    Google Scholar 

  17. Xu, H., Jiang, M., Li, F.: Depth estimation algorithm based on data-driven approach and depth cues for stereo conversion in three-dimensional displays. Opt. Eng. 55, 123106 (2016)

    Article  Google Scholar 

  18. Yao, G.S., Sun, S.Y., Fang, J.N.: Depth estimation of night unmanned vehicle scene based on infrared and radar. Laser Optoelectron. Progress. 312, 158–164 (2017)

    Google Scholar 

  19. Cai, B., Xu, X., Jia, K., Qing, C., Tao, D.: DehazeNet: an end-to-end system for single image haze removal. IEEE Trans. Image Process. 299, 5187–5198 (2016)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jiawei Tan .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Cheng, Y., Dong, Y., Tan, J. (2019). The Overview of 2D to 3D Automatic Conversion. In: Wang, Y., Huang, Q., Peng, Y. (eds) Image and Graphics Technologies and Applications. IGTA 2019. Communications in Computer and Information Science, vol 1043. Springer, Singapore. https://doi.org/10.1007/978-981-13-9917-6_12

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-9917-6_12

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-9916-9

  • Online ISBN: 978-981-13-9917-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics