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
A perceptually based physical error metric for realistic image synthesis
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
Statistically optimized sampling for distributed ray tracing
SIGGRAPH '85 Proceedings of the 12th annual conference on Computer graphics and interactive techniques
A ray tracing solution for diffuse interreflection
SIGGRAPH '88 Proceedings of the 15th annual conference on Computer graphics and interactive techniques
Realistic image synthesis using photon mapping
Realistic image synthesis using photon mapping
Principles of Digital Image Synthesis
Principles of Digital Image Synthesis
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Adaptive Smpling and Bias Estimation in Path Tracing
Proceedings of the Eurographics Workshop on Rendering Techniques '97
Lighting Reconstruction Using Fast and Adaptive Density Estimation Techniques
Proceedings of the Eurographics Workshop on Rendering Techniques '97
Density estimation techniques for global illumination
Density estimation techniques for global illumination
Lightcuts: a scalable approach to illumination
ACM SIGGRAPH 2005 Papers
A first-order analysis of lighting, shading, and shadows
ACM Transactions on Graphics (TOG)
ACM SIGGRAPH Asia 2008 papers
Stochastic progressive photon mapping
ACM SIGGRAPH Asia 2009 papers
Irradiance gradients in the presence of participating media and occlusions
EGSR'08 Proceedings of the Nineteenth Eurographics conference on Rendering
Ray maps for global illumination
EGSR'05 Proceedings of the Sixteenth Eurographics conference on Rendering Techniques
Progressive photon mapping: A probabilistic approach
ACM Transactions on Graphics (TOG)
Proceedings of the 2011 SIGGRAPH Asia Conference
NoRM: No-Reference Image Quality Metric for Realistic Image Synthesis
Computer Graphics Forum
Noise reduction for progressive photon mapping
ACM SIGGRAPH 2012 Talks
State of the art in photon density estimation
ACM SIGGRAPH 2012 Courses
Light transport simulation with vertex connection and merging
ACM Transactions on Graphics (TOG) - Proceedings of ACM SIGGRAPH Asia 2012
SURE-based optimization for adaptive sampling and reconstruction
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
Progressive expectation-maximization for hierarchical volumetric photon mapping
EGSR'11 Proceedings of the Twenty-second Eurographics conference on Rendering
Adaptive progressive photon mapping
ACM Transactions on Graphics (TOG)
Rendering scenes with participating media based on RBFs for photon mapping using graphics hardware
Proceedings of the 12th ACM SIGGRAPH International Conference on Virtual-Reality Continuum and Its Applications in Industry
Special Section on CAD/Graphics 2013: Adaptive importance photon shooting technique
Computers and Graphics
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We present an error estimation framework for progressive photon mapping. Although estimating rendering error has been established for unbiased rendering algorithms, error estimation for biased rendering algorithms has not been investigated well in comparison. We characterize the error by the sum of a bias estimate and a stochastic noise bound, which is motivated by stochastic error bounds formulation in biased methods. As a part of our error computation, we extend progressive photon mapping to operate with smooth kernels. This enables the calculation of illumination gradients with arbitrary accuracy, which we use to progressively compute the local bias in the radiance estimate. We also show how variance can be computed in progressive photon mapping, which is used to estimate the error due to noise. As an example application, we show how our error estimation can be used to compute images with a given error threshold. For this example application, our framework only requires the error threshold and a confidence level to automatically terminate rendering. Our results demonstrate how our error estimation framework works well in realistic synthetic scenes.