Skip to main content
Log in

An anaglyph 2D-3D stereoscopic video visualization approach

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

In this paper, we propose a simple anaglyph 3D stereo generation algorithm from 2D video sequence with a monocular camera. In our novel approach, we employ camera pose estimation method to directly generate stereoscopic 3D from 2D video without building depth map explicitly. Our cost-effective method is suitable for arbitrary real-world video sequence and produces smooth results. We use image stitching based on plane correspondence using fundamental matrix. To this end, we also demonstrate that correspondence plane image stitching based on Homography matrix only cannot generate a better result. Furthermore, we utilize the structure-from-motion (with fundamental matrix) based reconstructed camera pose model to accomplish visual anaglyph 3D illusion. The anaglyph result is visualized by a contour based yellow-blue 3D color code. The proposed approach demonstrates a very good performance for most of the video sequences in the user study.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Azzari L, Battisti F, Gotchev A, Carli M, Egiazarian K (2011) A modified non-local mean inpainting technique for occlusion filling in depth-image-based rendering. In Pro. SPIE, Stereoscopic Displays and Applications XXII, volume 7863

  2. Brown M, Lowe DG (2007) Automatic panoramic image stitching using invariant features. Int J Comput Vis 74(1):59–73

    Article  Google Scholar 

  3. Do L, Zinger S, de With PHN (2011) Warping error analysis and reduction for depth- image-based rendering in 3dtv. In Pro. SPIE, Stereoscopic Displays and Applications XXII, volume 7863

  4. Fradi H, Dugelay J (2011) Improved depth map estimation in stereo vision. In Pro. SPIE, Stereoscopic Displays and Applications XXII, volume 7863

  5. Hartley RI (1999) Theory and practice of projective rectification. Int J Comput Vis 35(2):115–127

    Article  Google Scholar 

  6. Hartley R, Zisserman A (2003) Multiple view geometry in computer vision. Cambridge University Press, New York

    MATH  Google Scholar 

  7. Kurz C, Thormählen T, Seidel H (2011) Bundle adjustment for stereoscopic 3d. In Proc. 5th int. conf. Computer vision/computer graphics collaboration techniques

  8. Kutulakos KN, Vallino JR (1998) Calibration-free augmented reality. IEEE Trans Vis Comput Graph pages 1–20

    Article  Google Scholar 

  9. Lepetit V, Fua P (2006) Keypoint recognition using randomized trees. IEEE Trans Pattern Anal Mach Intell 28(9):1465–1479

    Article  Google Scholar 

  10. Loop C, Zhengyou Z (1999) Computing rectifying homographies for stereo vision. In IEEE Computer Vision and Pattern Recognition(CVPR), volume 1

  11. Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110

    Article  Google Scholar 

  12. McKay HC (1951) Three-dimensional photography: principles of steroscopy. American Photography

  13. Muja M, Lowe DG (2009) Fast approximate nearest neighbors with automatic algorithm configuration. In Proc. Int. Conf. . Computer Vision Theory and Application (VISSAPP’09), pages 331–340

  14. Niquin C, Pevost S, Remion Y (2010) A point cloud based pipeline for depth reconstruction from autostereoscopic sets. In Pro. SPIE, Stereoscopic Displays and Applications XXI, volume 7524

  15. Robertson DP, Cipolla R (2009) Structure from Motion. Practical Image Processing and Computer Vision, John Wiley

  16. Shi R, Gan Y, Wang Y (2018) Evaluating scalability bottlenecks by workload extrapolation. In 2018 IEEE 26th International Symposium on Modelling, Analysis and Simulation of Computer and Telecommunication Systems (MASCOTS), Milwaukee, WI, USA, Sept

  17. Spottiswoode R, Spottiswoode N (1993) The Theory of Stereoscopic Transmission & Its Application to the Motion Picture. Cambridge University Press

  18. Torr PHS, Murray DW (1997) The development and comparison of robust methodsfor estimating the fundamental matrix. Int J Comput Vision pages 271–300

  19. Yousefi S, Kondori F, Li H (2011) 3d gestural interaction for stereoscopic visualization on mobile devices. In 14 Computer Analysis of Images and Patterns (CAIP), pages 555–562

    Chapter  Google Scholar 

  20. Zisserman A, Fitzgibbon A, Cross G (1999) Vhs to vrml: 3d graphical models from video sequences. In Pro. IEEE Int. Conf. Multimedia Computing and Systems - Volume 2

Download references

Acknowledgements

This research is supported by Shandong Provincial Natural Science Foundation (ZR2017QF015) and National Natural Science Foundation of China (No. 61902203).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Zhihan Lv.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lv, Z., ur Réhman, S., Khan, M.S.L. et al. An anaglyph 2D-3D stereoscopic video visualization approach. Multimed Tools Appl 79, 825–838 (2020). https://doi.org/10.1007/s11042-019-08172-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-019-08172-1

Keywords

Navigation