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
SIGGRAPH '84 Proceedings of the 11th annual conference on Computer graphics and interactive techniques
Energy redistribution path tracing
ACM SIGGRAPH 2005 Papers
Advanced Global Illumination
Multidimensional adaptive sampling and reconstruction for ray tracing
ACM SIGGRAPH 2008 papers
ACM SIGGRAPH Asia 2008 papers
ACM Transactions on Graphics (TOG)
Frequency analysis and sheared reconstruction for rendering motion blur
ACM SIGGRAPH 2009 papers
ACM SIGGRAPH Asia 2009 papers
Stochastic progressive photon mapping
ACM SIGGRAPH Asia 2009 papers
Physically Based Rendering, Second Edition: From Theory To Implementation
Physically Based Rendering, Second Edition: From Theory To Implementation
A progressive error estimation framework for photon density estimation
ACM SIGGRAPH Asia 2010 papers
Progressive photon mapping: A probabilistic approach
ACM Transactions on Graphics (TOG)
Robust adaptive photon tracing using photon path visibility
ACM Transactions on Graphics (TOG)
Metropolis photon sampling with optional user guidance
EGSR'05 Proceedings of the Sixteenth Eurographics conference on Rendering Techniques
Photorealistic image rendering with population monte carlo energy redistribution
EGSR'07 Proceedings of the 18th Eurographics conference on Rendering Techniques
ACM Transactions on Graphics (TOG) - SIGGRAPH 2012 Conference Proceedings
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This paper presents an improvement to the stochastic progressive photon mapping (SPPM), a method for robustly simulating complex global illumination with distributed ray tracing effects. Normally, similar to photon mapping and other particle tracing algorithms, SPPM would become inefficient when the photons are poorly distributed. An inordinate amount of photons are required to reduce the error caused by noise and bias to acceptable levels. In order to optimize the distribution of photons, we propose an extension of SPPM with a Metropolis-Hastings algorithm, effectively exploiting local coherence among the light paths that contribute to the rendered image. A well-designed scalar contribution function is introduced as our Metropolis sampling strategy, targeting at specific parts of image areas with large error to improve the efficiency of the radiance estimator. Experimental results demonstrate that the new Metropolis sampling based approach maintains the robustness of the standard SPPM method, while significantly improving the rendering efficiency for a wide range of scenes with complex lighting.