SIGGRAPH '86 Proceedings of the 13th annual conference on Computer graphics and interactive techniques
Optimally combining sampling techniques for Monte Carlo rendering
SIGGRAPH '95 Proceedings of the 22nd annual conference on Computer graphics and interactive techniques
Proceedings of the 24th annual conference on Computer graphics and interactive techniques
Global illumination using photon maps
Proceedings of the eurographics workshop on Rendering techniques '96
Robust monte carlo methods for light transport simulation
Robust monte carlo methods for light transport simulation
Energy redistribution path tracing
ACM SIGGRAPH 2005 Papers
ACM SIGGRAPH Asia 2008 papers
Statistics and Computing
Stochastic progressive photon mapping
ACM SIGGRAPH Asia 2009 papers
Metropolis photon sampling with optional user guidance
EGSR'05 Proceedings of the Sixteenth Eurographics conference on Rendering Techniques
ACM Transactions on Graphics (TOG) - SIGGRAPH 2012 Conference Proceedings
A path space extension for robust light transport simulation
ACM Transactions on Graphics (TOG) - Proceedings of ACM SIGGRAPH Asia 2012
Light transport simulation with vertex connection and merging
ACM Transactions on Graphics (TOG) - Proceedings of ACM SIGGRAPH Asia 2012
Improved stochastic progressive photon mapping with metropolis sampling
EGSR'11 Proceedings of the Twenty-second Eurographics conference on Rendering
Adaptive progressive photon mapping
ACM Transactions on Graphics (TOG)
Visibility-driven progressive volume photon tracing
The Visual Computer: International Journal of Computer Graphics
Special Section on CAD/Graphics 2013: Adaptive importance photon shooting technique
Computers and Graphics
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We present a new adaptive photon tracing algorithm which can handle illumination settings that are considered difficult for photon tracing approaches such as outdoor scenes, close-ups of a small part of an illuminated region, and illumination coming through a small gap. The key contribution in our algorithm is the use of visibility of photon path as the importance function which ensures that our sampling algorithm focuses on paths that are visible from the given viewpoint. Our sampling algorithm builds on two recent developments in Markov chain Monte Carlo methods: adaptive Markov chain sampling and replica exchange. Using these techniques, each photon path is adaptively mutated and it explores the sampling space efficiently without being stuck at a local peak of the importance function. We have implemented this sampling approach in the progressive photon mapping algorithm which provides visibility information in a natural way when a photon path contributes to a measurement point. We demonstrate that the final algorithm is strikingly simple, yet effective at sampling photons under lighting conditions that would be difficult for existing Monte Carlo ray tracing-based methods.