skip to main content
10.1145/2804408.2804409acmconferencesArticle/Chapter ViewAbstractPublication PagessapConference Proceedingsconference-collections
research-article

What makes 2D-to-3D stereo conversion perceptually plausible?

Published:13 September 2015Publication History

ABSTRACT

Different from classic reconstruction of physical depth in computer vision, depth for 2D-to-3D stereo conversion is assigned by humans using semi-automatic painting interfaces and, consequently, is often dramatically wrong. Here we seek to better understand why it still does not fail to convey a sensation of depth. To this end, four typical disparity distortions resulting from manual 2D-to-3D stereo conversion are analyzed: i) smooth remapping, ii) spatial smoothness, iii) motion-compensated, temporal smoothness, and iv) completeness. A perceptual experiment is conducted to quantify the impact of each distortion on the plausibility of the 3D impression relative to a reference without distortion. Close-to-natural videos with known depth were distorted in one of the four above-mentioned aspects and subjects had to indicate if the distortion still allows for a plausible 3D effect. The smallest amounts of distortion that result in a significant rejection suggests a conservative upper bound on the quality requirement of 2D-to-3D conversion.

Skip Supplemental Material Section

Supplemental Material

References

  1. Assa, J., and Wolf, L. 2007. Diorama construction from single images. Comp. Graph. Forum (Proc. EG) 26, 3, 599--608.Google ScholarGoogle ScholarCross RefCross Ref
  2. Borji, A., and Itti, L. 2013. State-of-the-art in visual attention modeling. Pattern Analysis and Machine Intelligence, IEEE Transactions on 35, 1, 185--207. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Butler, D. J., Wulff, J., Stanley, G. B., and Black, M. J. 2012. A naturalistic open source movie for optical flow evaluation. In European Conf. on Computer Vision (ECCV), 611--625. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Cheng, C.-C., Li, C.-T., and Chen, L.-G. 2010. An ultra-low-cost 2D-to-3D video conversion system. SID 41, 1, 766--9.Google ScholarGoogle ScholarCross RefCross Ref
  5. Dabala, Ł., Kellnhofer, P., Ritschel, T., Didyk, P., Templin, K., Myszkowski, K., Rokita, P., and Seidel, H.-P. 2014. Manipulating refractive and reflective binocular disparity. Comp. Graph. Forum (Proc. Eurographics 2014) 33, 2, 53--62. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Didyk, P., Ritschel, T., Eisemann, E., Myszkowski, K., and Seidel, H.-P. 2011. A perceptual model for disparity. ACM Trans. Graph. (Proc. SIGGRAPH) 30, 96:1--96:10. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Didyk, P., Ritschel, T., Eisemann, E., Myszkowski, K., Seidel, H.-P., and Matusik, W. 2012. A luminance-contrast-aware disparity model and applications. ACM Trans. Graph. (Proc. SIGGRAPH Asia) 31, 6. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Fehn, C. 2004. Depth-image-based rendering (DIBR), compression, and transmission for a new approach on 3D-TV. In Stereoscopic Displays and Virtual Reality Systems XI, SPIE, vol. 5291, 93--104.Google ScholarGoogle Scholar
  9. Guttmann, M., Wolf, L., and Cohen-Or, D. 2009. Semiautomatic stereo extraction from video footage. In Proc. ICCV, 136--142.Google ScholarGoogle Scholar
  10. Hoiem, D., Efros, A. A., and Hebert, M. 2005. Automatic photo pop-up. ACM Trans. Graph. 24, 3, 577--584. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Howard, I., and Rogers, B. 2012. Perceiving in Depth, Volume 2: Stereoscopic Vision. Oxford Psychology Series.Google ScholarGoogle Scholar
  12. Huang, X., Wang, L., Huang, J., Li, D., and Zhang, M. 2009. A depth extraction method based on motion and geometry for 2D to 3D conversion. In Proc. IITA, 294--298. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Itti, L., Koch, C., and Niebur, E. 1998. A model of saliency-based visual attention for rapid scene analysis. IEEE PAMI 20, 11, 1254--9. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Jones, G., Lee, D., Holliman, N., and Ezra, D. 2001. Controlling perceived depth in stereoscopic images. In SPIE, vol. 4297, 42--53.Google ScholarGoogle Scholar
  15. Kane, D., Guan, P., and Banks, M. 2014. The limits of human stereopsis in space and time. J Neurosc. 34, 4, 1397--408.Google ScholarGoogle ScholarCross RefCross Ref
  16. Karsch, K., Liu, C., and Kang, S. B. 2014. Depth transfer: Depth extraction from video using non-parametric sampling. IEEE PAMI 36, 11, 2144--58.Google ScholarGoogle ScholarCross RefCross Ref
  17. Konrad, J., Wang, M., and Ishwar, P. 2012. 2D-to-3D image conversion by learning depth from examples. In CVPR, 16--22.Google ScholarGoogle Scholar
  18. Kopf, J., Cohen, M. F., Lischinski, D., and Uyttendaele, M. 2007. Joint bilateral upsampling. ACM Trans. Graph. (Proc. SIGGRAPH) 26, 3. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Lang, M., Hornung, A., Wang, O., Poulakos, S., Smolic, A., and Gross, M. 2010. Nonlinear disparity mapping for stereoscopic 3D. ACM Trans. Graph. (Proc. SIGGRAPH) 29, 4. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Lang, M., Wang, O., Aydin, T., Smolic, A., and Gross, M. 2012. Practical temporal consistency for image-based graphics applications. ACM Trans. Graph. (Proc. SIGGRAPH) 31, 4. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Liu, X., Mao, X., Yang, X., Zhang, L., and Wong, T.-T. 2013. Stereoscopizing cel animations. ACM Trans. Graph. (Proc. SIGGRAPH Asia) 32, 6, 223. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Merkle, P., Morvan, Y., Smolic, A., Farin, D., Müller, K., de With, P. H. N., and Wiegand, T. 2009. The effects of multiview depth video compression on multiview rendering. Signal Processing: Image Communcation 24, 1--2. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. Murata, H., Mori, Y., Yamashita, S., Maenaka, A., Okada, S., Oyamada, K., and Kishimoto, S. 1998. A real-time 2-D to 3-D image conversion technique using computed image depth. SID 29, 1, 919--23.Google ScholarGoogle ScholarCross RefCross Ref
  24. Pajak, D., Herzog, R., Mantiuk, R., Didyk, P., Eisemann, E., Myszkowski, K., and Pulli, K. 2014. Perceptual depth compression for stereo applications. Computer Graphics Forum (Proc. Eurographics) 33, 2. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Ramanarayanan, G., Ferwerda, J., Walter, B., and Bala, K. 2007. Visual equivalence: Towards a new standard for image fidelity. ACM Trans. Graph. (Proc. SIGGRAPH) 26, 3. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Richardt, C., Stoll, C., Dodgson, N., Seidel, H.-P., and Theobalt, C. 2012. Coherent spatiotemporal filterung, upsampling and rendering of RGBZ videos. Comp. Graph. Forum 31, 2. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Robinson, A. E., and MacLeod, D. I. A. 2013. Depth and luminance edges attract. Journal of Vision 13, 11.Google ScholarGoogle ScholarCross RefCross Ref
  28. Saxena, A., Sun, M., and Ng, A. Y. 2009. Make3D: Learning 3D scene structure from a single still image. PAMI 31, 5, 824--40. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Shinya, M. 1993. Spatial anti-aliasing for animation sequences with spatio-temporal filtering. In Proc. SIGGRAPH, 289--96. Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. Wandell, B. A. 1995. Foundations of vision. Sinauer Associates.Google ScholarGoogle Scholar
  31. Wang, Z., Bovik, A. C., Sheikh, H. R., and Simoncelli, E. P. 2004. Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Processing 13, 4, 600--12. Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. Ward, B., Kang, S. B., and Bennett, E. 2011. Depth director: A system for adding depth to movies. IEEE Comp. Graph. and App. 31, 1, 36--48. Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. Yamada, K., and Suzuki, Y. 2009. Real-time 2D-to-3D conversion at full HD 1080p resolution. In IEEE ISCE, 103--106.Google ScholarGoogle Scholar
  34. Yang, Z., and Purves, D. 2003. A statistical explanation of visual space. Nature Neuroscience 6, 6, 632--640.Google ScholarGoogle ScholarCross RefCross Ref
  35. Zhang, G., Hua, W., Qin, X., Wong, T.-T., and Bao, H. 2007. Stereoscopic video synthesis from a monocular video. IEEE TVCG 13, 4, 686--96. Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. Zhang, L., Vazquez, C., and Knorr, S. 2011. 3D-TV content creation: Automatic 2D-to-3D video conversion. IEEE Trans. Broadcasting 57, 2, 372--83.Google ScholarGoogle ScholarCross RefCross Ref

Index Terms

  1. What makes 2D-to-3D stereo conversion perceptually plausible?

      Recommendations

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in
      • Published in

        cover image ACM Conferences
        SAP '15: Proceedings of the ACM SIGGRAPH Symposium on Applied Perception
        September 2015
        139 pages
        ISBN:9781450338127
        DOI:10.1145/2804408

        Copyright © 2015 ACM

        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 13 September 2015

        Permissions

        Request permissions about this article.

        Request Permissions

        Check for updates

        Qualifiers

        • research-article

        Acceptance Rates

        Overall Acceptance Rate43of94submissions,46%

        Upcoming Conference

        SAP '24
        ACM Symposium on Applied Perception 2024
        August 30 - 31, 2024
        Dublin , Ireland

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader