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
Direct Lighting
1) 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
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 E
p
is larger than E
min
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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