Download - Paper Presentation - An Efficient GPU-based Approach for Interactive Global Illumination-
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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
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Overview
● Previous work
● Main Algorithm● GPU-based KD-Tree● Selecting Irradiance Sample Points● Reducing the Cost of Final Gather
● Results
● Conclusion
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Overview
● Previous work
● Main Algorithm● GPU-based KD-Tree● Selecting Irradiance Sample Points● Reducing the Cost of Final Gather
● Results
● Conclusion
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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
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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]
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Overview
● Previous work
● Main Algorithm● GPU-based KD-Tree● Selecting Irradiance Sample Points● Reducing the Cost of Final Gather
● Results
● Conclusion
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System Overview
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Overview
● Previous work
● Main Algorithm● GPU-based KD-Tree● Selecting Irradiance Sample Points● Reducing the Cost of Final Gather
● Results
● Conclusion
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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
Direct Lighting1) Build a kd-tree of the scene, and trace eye rays in parallel
2) Collect rays that hit non-specular surfaces using a parallel list compaction [Harris et al. 2007]
3) Collect rays that hit specular surfaces, and spawn reflected and refracted rays for them
4) Repeat steps 2 and 3 for additional bounces
5) For all non-specular hit points, perform shadow tests and compute direct shading in parallel
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Overview
● Previous work
● Main Algorithm● GPU-based KD-Tree● Selecting Irradiance Sample Points● Reducing the Cost of Final Gather
● Results
● Conclusion
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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.
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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.
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A parallel view space sampling strategy
● Clustering optimization
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A parallel view space sampling strategy
● Clustering optimization● Error metric :
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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.
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Result
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Overview
● Previous work
● Main Algorithm● GPU-based KD-Tree● Selecting Irradiance Sample Points● Reducing the Cost of Final Gather
● Results
● Conclusion
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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.
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A cut approximation on photon map
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A cut approximation on photon map
● Select node which Ep is larger than Emin
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A cut approximation on photon map
● Refinement with threshold
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Result
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Overview
● Previous work
● Main Algorithm● GPU-based KD-Tree● Selecting Irradiance Sample Points● Reducing the Cost of Final Gather
● Results
● Conclusion
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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
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Results
Ours Reference 8 times
error Image
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Results
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Overview
● Previous work
● Main Algorithm● GPU-based KD-Tree● Selecting Irradiance Sample Points● Reducing the Cost of Final Gather
● Results
● Conclusion
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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
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Q&A
● Thank you.