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Paper Presentation - An Efficient GPU-based Approach for Interactive Global Illumination-

Paper Presentation - An Efficient GPU-based Approach for Interactive Global Illumination-. Rui Wang, Rui Wang, Kun Zhou, Minghao Pan, Hujun Bao Presenter : Jong Hyeob Lee 2010. 11. 23. Overview. Previous work Main Algorithm GPU-based KD-Tree Selecting Irradiance Sample Points

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Paper Presentation - An Efficient GPU-based Approach for Interactive Global Illumination-

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  1. Paper Presentation - An Efficient GPU-based Approach for Interactive Global Illumination- Rui Wang, Rui Wang, Kun Zhou, Minghao Pan, Hujun Bao Presenter : Jong Hyeob Lee 2010. 11. 23

  2. Overview • Previous work • Main Algorithm • GPU-based KD-Tree • Selecting Irradiance Sample Points • Reducing the Cost of Final Gather • Results • Conclusion

  3. Overview • Previous work • Main Algorithm • GPU-based KD-Tree • Selecting Irradiance Sample Points • Reducing the Cost of Final Gather • Results • Conclusion

  4. Previous work • CPU-based global illumination • Instant radiosity [Keller 1997] • Photon mapping [Jensen 2001] • Interactive global illumination using fast ray tracing [Wald et al. 2002] • LightCuts [Walter et al. 2005] Radiosity Photon mapping

  5. Previous work • GPU-based global illumination • Reflective shadow maps [Dachsbacher and Stamminger 2005] • Radiance Cache Splatting [Gautron et al. 2005] • Matrix row-column sampling [Hasan et al. 2007] • Imperfect shadow maps [Ritschel et al. 2008] • GPU KD-Tree construction [Zhou et al. 2008]

  6. Overview • Previous work • Main Algorithm • GPU-based KD-Tree • Selecting Irradiance Sample Points • Reducing the Cost of Final Gather • Results • Conclusion

  7. System Overview

  8. Overview • Previous work • Main Algorithm • GPU-based KD-Tree • Selecting Irradiance Sample Points • Reducing the Cost of Final Gather • Results • Conclusion

  9. GPU-based KD-Tree • Use method in “Real-time kd-tree construction on graphics hardware” [Zhou et al. 2008] • To build kd-trees in real-time using NVIDIA’s CUDA

  10. Overview • Previous work • Main Algorithm • GPU-based KD-Tree • Selecting Irradiance Sample Points • Reducing the Cost of Final Gather • Results • Conclusion

  11. A parallel view space sampling strategy • The goal of view space sampling: • Select sample points that best approximate the actual (ir)radiance changes in view space.

  12. A parallel view space sampling strategy • Irradiance caching [Ward et al. 1998] • Progressively inserting sample points into an existing set. • Decision to insert more samples is based on the local variations of irradiance samples.

  13. A parallel view space sampling strategy • Clustering optimization

  14. A parallel view space sampling strategy • Clustering optimization • Error metric :

  15. A parallel view space sampling strategy • Temporal coherence • Fix cluster centers computed from the previous frame. • Classify shading points to these clusters. • Collect points with large errors. • Create new cluster for these unclassified shading points and remove null clusters.

  16. Result

  17. Overview • Previous work • Main Algorithm • GPU-based KD-Tree • Selecting Irradiance Sample Points • Reducing the Cost of Final Gather • Results • Conclusion

  18. A cut approximation on photon map • Computing an illumination cut from the photon tree. • Typical approach: density estimation for each photon → too costly • Estimate an illumination cut from the photon map directly, without density estimation at each photon.

  19. A cut approximation on photon map

  20. A cut approximation on photon map • Select node which Ep is larger than Emin

  21. A cut approximation on photon map • Refinement with threshold

  22. Result

  23. Overview • Previous work • Main Algorithm • GPU-based KD-Tree • Selecting Irradiance Sample Points • Reducing the Cost of Final Gather • Results • Conclusion

  24. Results • Implemented on BGSP [Hou et al. 2008] • A general purpose C programming interface suitable for many core architecture such as the GPU • Point or spot cone lights • 3 bounces (2 photon bounces and final gather) • 250 ~ 500 final gather rays

  25. Results Ours Reference 8 times error Image

  26. Results

  27. Overview • Previous work • Main Algorithm • GPU-based KD-Tree • Selecting Irradiance Sample Points • Reducing the Cost of Final Gather • Results • Conclusion

  28. Conclusion • An efficient GPU-based method for interactive global illumination is presented. • Sparse view space (ir)radiance sampling • A cut approximation of the photon map • A GPU approach of interactive global illumination • Limitations • Only glossy materials for final gather • Missing small geometric details • With some temporal flickering artifacts

  29. Q&A • Thank you.

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