Acceleration of Monte Carlo Path Tracing in General Environments

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Acceleration of Monte Carlo PathTracing in
General Environments
Frederic Pérez1Ignacio Martín1François X.
Sillion2Xavier Pueyo1
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Outline

• Importance Sampling
• Previous work
• Link Probabilities
• Results
• Conclusions
• Future Work

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Importance Sampling

• Variance reduction technique
• Use the best samples to evaluate an integral
  Probability Density Function (PDF) prop. to
  kernel
• Reflectance equation
• Importance sampling
• Use of approximate for PDF ?
  Usually by means of a 1st pass

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Previous Work

• Chen et al. 91
• Progressive Multi-Pass
• Jensen 95
• Importance Driven Photon Maps
• Szirmay-Kalos et al. 98
• Importance Driven Quasi-Random walk
• Stürzlinger 96, Ureña Torres 97
• Final Gathering

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Shortcomings

• Fixed subdivision of hemisphere
• Cannot adapt to irradiance changes
• Missing samples
• Sources does not fit solid angles
• Fixed subdivision of object space first pass

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Link Probabilities Overview

• 1st Pass Radiance Clustering
• Produces Line-Space hierarchy Links
• Coarse solution not for rendering
• 2nd Pass Monte Carlo Path Tracing
• Improved Importance Sampling with Link
  Probabilities
• Use links arriving at leaves as a representation
  of the irradiance

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Link Probabilities

• Importance Sampling in a random walk step

for each bounce at point x
x
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Link Probabilities PDF per leaf?

• Using a PDF per leaf ? Artifacts for 1st
  bounce

? Solution Recompute PDF per pixel
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Link Probabilities

• Link overlap

S2
S1
y
? Check if links sender is hit
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Link Probabilities

• PDF accuracy and visibility

S1
l1
S2
l2
R
Finite elements pass ? two links arriving at
leaf R
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Link Probabilities

• PDF accuracy and visibility

Solution Adaptive PDFs
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Results

• Simple test scene

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Results

• Room with indirect illumination

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Results
16 samples
128 samples
64 samples
NEE
806s
1613s
201s
LP
1273s
2434s
409s
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Conclusions

• Enhancement of Path Tracing by Importance
  Sampling based on the estimated irradiances
  computed with a Radiance Clustering 1st pass
• Quality of the images doesnt strongly depend on
  the lighting conditions

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Future Work

• Extension for isotropic participating media
• Account for links arriving to leaf clusters with
  media and sample directions as for surfaces
• Addition of glossy surfaces and anisotropic
  participating media
• Account for BRDF/phase function
• Use of Link Probabilities in BPT
• Eye paths as presented
• Light paths similarly using importance
• Comparison to Metropolis Light transport

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Acknowledgments

• SIMULGEN ESPRIT project 35772
• http//iiia.udg.es/Simulgen
• Generalitat de Catalunyas AIRE
• CICYTs TIC98-0586-C03-02