Analysis of Sample Correlations for Monte Carlo Rendering David - - PowerPoint PPT Presentation

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analysis of sample correlations for monte carlo rendering

Analysis of Sample Correlations for Monte Carlo Rendering David - - PowerPoint PPT Presentation

Dec 21, 2023 798 likes •1.12k views

Analysis of Sample Correlations for Monte Carlo Rendering David Coeurjolly Gurprit Singh Cengiz Oztireli Abdalla G. Ahmed Kartic Subr Oliver Deussen Victor Ostromoukhov Ravi Ramamoorthi Wojciech Jarosz Gurprit Singh Cengiz Oztireli

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Analysis of Sample Correlations for Monte Carlo Rendering

Wojciech Jarosz Ravi Ramamoorthi Victor Ostromoukhov Oliver Deussen Kartic Subr Abdalla G. Ahmed David Coeurjolly Cengiz Oztireli Gurprit Singh

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Wojciech Jarosz Ravi Ramamoorthi Victor Ostromoukhov Oliver Deussen Kartic Subr Abdalla G. Ahmed David Coeurjolly Cengiz Oztireli Gurprit Singh

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Gurprit Singh Cengiz Oztireli Wojciech Jarosz Ravi Ramamoorthi Victor Ostromoukhov Oliver Deussen Kartic Subr Abdalla G. Ahmed David Coeurjolly

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Rendering = Geometry + Radiometry

Geometry / Projection for pin-hole model is known since 400BC

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Rendering = Geometry + Radiometry

Geometry / Projection Radiometrically accurate simulation is importance of realism for pin-hole model is known since 400BC

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Rendering = Geometry + Radiometry

Geometry / Projection Radiometrically accurate simulation is importance of realism for pin-hole model is known since 400BC

OpenGL

[Stachowiak 2010]

Raytracing

[Whitted 1980]

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Radiometric fidelity improves photorealism

Papas et al. [2013]

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Radiometric fidelity improves photorealism

Krivanek et al. [2014]

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Reconstruction: Estimate image samples

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Ground truth (high-res) image Reconstruct on (low-res) pixel grid

Naive method: sample image at grid locations

Copy

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Ground truth (high-res) image Reconstruct on (low-res) pixel grid

Naive method: sample image at grid locations

Aliasing

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Ground truth (high-res) image Reconstruct on (low-res) pixel grid

Naive method: sample image at grid locations

Average

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Ground truth (high-res) image Reconstruct on (low-res) pixel grid

Antialiasing using general reconstruction filters

Weighted Average

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Ground truth (high-res) image Reconstruct on (low-res) pixel grid

Naive method: sample image at grid locations

Weighted Average

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Rendering: reconstructing integrals

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Rendering: reconstructing integrals

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Rendering: reconstructing integrals

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Rendering: reconstructing integrals

Each path has an associated radiance value

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Global Illumination: Participating media

Each path has an associated radiance value

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s-dimensional path space Pixel sensor

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s-dimensional path space Pixel sensor

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s-dimensional path space Pixel sensor

Path-space integration (projection)

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s-dimensional path space Pixel sensor Pixel sensor Pixel radiance value

Reconstruction using integrated radiance Path-space integration

Rendering = integration + reconstruction

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s-dimensional path space Pixel sensor Pixel sensor Pixel radiance value

Reconstruction filter Local variation of the integrand

Frequency analysis of light fields in rendering

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s-dimensional path space

This STAR: Analyze sample correlations for MC sampling

Pixel sensor Assessing MSE, bias, variance and convergence

f Monte Carlo estimators using

spatial and spectral tools

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This STAR: Analyze sample correlations for MC sampling

Fredo Durand [2011] Subr and Kautz [2013] Subr et al. [2014] Pilleboue et al. Georgiev & Fajardo [2015] Cengiz Oztireli [2016] Singh & Jarosz [2017a] Singh et al. [2017b] Singh et al. [2019] Ramamoorthi et al. [2012]

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Sample correlations affect light transport / appearance

Jarabo et al. [2018] Bitterli et al. [2018] Guo et al. [2019]

Non-exponential media Traditional exponential media

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Theoretical Tools Point Processes Fourier transform / Series Samples Quality Assessment Spatial Domain Formulations Fourier Domain Formulations Pair Correlation Function Fourier Transform / Series Error Formulations Error Analysis Stratification Strategies Low Discrepancy Samplers Stochastic Samplers