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.