3D Research

, 9:7 | Cite as

Parallel Computer System for 3D Visualization Stereo on GPU

3DR Express
  • 72 Downloads

Abstract

This paper proposes the organization of a parallel computer system based on Graphic Processors Unit (GPU) for 3D stereo image synthesis. The development is based on the modified ray tracing method developed by the authors for fast search of tracing rays intersections with scene objects. The system allows significant increase in the productivity for the 3D stereo synthesis of photorealistic quality. The generalized procedure of 3D stereo image synthesis on the Graphics Processing Unit/Graphics Processing Clusters (GPU/GPC) is proposed. The efficiency of the proposed solutions by GPU implementation is compared with single-threaded and multithreaded implementations on the CPU. The achieved average acceleration in multi-thread implementation on the test GPU and CPU is about 7.5 and 1.6 times, respectively. Studying the influence of choosing the size and configuration of the computational Compute Unified Device Archi-tecture (CUDA) network on the computational speed shows the importance of their correct selection. The obtained experimental estimations can be significantly improved by new GPUs with a large number of processing cores and multiprocessors, as well as optimized configuration of the computing CUDA network.

Keywords

3D visualization Ray tracing 3D stereo image Synthesis Graphics processor Computer system 

References

  1. 1.
    Al-Oraiqat, A., & Zori, S. (2016). 3D-visualization by raytracing image synthesis on GPU. International Journal of Engineering Science and Technology (IJEST), 8(6), 97–104.Google Scholar
  2. 2.
    Al-Oraiqat, A., Bashkov, E., & Zori, S. (2018). Spatial visualization via real time 3D volumetric display technologies (p. 120). Saarbrücken: LAP LAMBERT Academic Publishing.Google Scholar
  3. 3.
    Bashkov, E., & Zori, S. (2016). Systems of spatial visualization of environment. Science Bulletin of Donetsk National Technical University, Krasnoarmiysk, 1(1), 20–45.Google Scholar
  4. 4.
    Blundell, B., & Schwarz, A. (2002). The classification of volumetric display systems, characteristics and predictability of the image space. IEEE Transactions on Visualization and Computer Graphics, 8(1), 66–75.CrossRefGoogle Scholar
  5. 5.
    Bogolepov, D., Ulyanov, D., Sopin, D., & Turlapov, V. (2013). Optimization of the bidirectional path trace method for modeling the optical experiment on a graphics processor. Scientific Visualization, 2(5), 1–15.Google Scholar
  6. 6.
    Cook, R., Porter, T., & Carpenter, L. (1984). Distributed ray tracing. ACM SIGGRAPH Computer Graphics, 18, 137–145. https://dl.acm.org/citation.cfm?id=808590.
  7. 7.
    CUDA. (2007). Occupancy Calculator Helps pick optimal thread block size. https://devtalk.nvidia.com/default/topic/368105/cuda–occupancy–calculator–helps–pick–optimal–thread–block–size/. Accessed Jul 11, 2017.
  8. 8.
    Hong, G.-S., Hoe, W., Kim, B.-G., Beak, J.-W., & Kwon, K.-K. (2016). Stereo matching performance analysis of cost functions on the graphic processing unit (GPU) for pervasive computing. Journal of Engineering and Applied Sciences, 11(7), 1480–1487.Google Scholar
  9. 9.
    Hong, G.-S., & Kim, B.-G. (2017). Novel local stereo matching technique based on weighted guided image filtering (WGIF). Displays (Elsevier), 49, 80–87.  https://doi.org/10.1016/j.displa.2017.07.006.CrossRefGoogle Scholar
  10. 10.
    Meyer, L., & Klassen, R. (1998). A comparison of two image quality models. In Human Vision and Electronic Imaging III (Vol. 3299, pp. 98–109). SPIE.  https://doi.org/10.1117/12.320101.
  11. 11.
    Murdock, K. L. (2010). 3Ds Max 2010 Bible (p. 1312). Mission: SDC Publications. https://dl.acm.org/citation.cfm?id=1795691.
  12. 12.
    Samarin, A. (2005). Modern three-dimensional image display technologies. Modern Electronics, 2, 2–7.Google Scholar
  13. 13.
    Shirley, P., & Morley, R. (2003). Realistic ray tracing (Vol. 235). Boca Raton: CRC Press.Google Scholar
  14. 14.
    Zori, S. A. (2015). Volumetric visualization by ray tracing algorithm with two-level hierarchy of limiting volumes and AABB. Bulletin of DonNTU, Donetsk, 2(21), 5–10.Google Scholar
  15. 15.
    Zori, S. A. (2016). GPU-implementation of parallel computing system 3D stereo imaging using the ray tracing method. Information Processing Systems: Collected Works, Kharkiv, 6(143), 201–204.Google Scholar
  16. 16.
    Zori, S., & Porfirov, P. (2015). Productivity increasing of realistic ray tracing stereo-image synthesis. Journal of Qafqaz University, Mathematics and Computer Science, 3(1), 30–38.Google Scholar
  17. 17.
    Zori, S., Zaporozhchenko, I., & Grigorev, M. (2013). Analysis of ways to reduce the computational complexity of ray tracing algorithm and methods of its parallel implementation. Digital Signal and Image Processing, 4, 352–357.Google Scholar

Copyright information

© 3D Research Center, Kwangwoon University and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer Sciences and InformationTaibah UniversityMedinaKingdom of Saudi Arabia
  2. 2.Department of Computer Sciences and TechnologiesSHEE «Donetsk National Technical University»PokrovskyUkraine

Personalised recommendations