1 / 29

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

rowdy
Download Presentation

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

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  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.

More Related