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What and Who
Title:Analyzing Sample Correlations for Monte Carlo Rendering
Speaker:Gurprit Singh
coming from:Max-Planck-Institut für Informatik - D4
Speakers Bio:
Event Type:Joint Lecture Series
Visibility:D1, D2, D3, INET, D4, D5, SWS, RG1, MMCI
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Level:Public Audience
Date, Time and Location
Date:Wednesday, 13 March 2019
Duration:60 Minutes
Building:E1 5
Point patterns and stochastic structures lie at the heart of Monte Carlo based numerical integration schemes. Physically based rendering algorithms have largely benefited from these Monte Carlo based schemes that inherently solve very high dimensional light transport integrals. However, due to the underlying stochastic nature of the samples, the resultant images are corrupted with noise (unstructured aliasing or variance). This also results in bad convergence rates that prohibit using these techniques in more interactive environments (e.g. games, virtual reality). With the advent of smart rendering techniques and powerful computing units (CPUs/GPUs), it is now possible to perform physically based rendering at interactive rates. However, much is left to understand regarding the underlying sampling structures and patterns which are the primary cause of error in rendering. 

In this talk, we first revisit the most recent state-of-the-art frameworks that are developed to better understand the impact of samples’ structure on the error and its convergence during Monte Carlo integration. Towards the end, we briefly present our deep learning based approach to generate these samples with correlations.

Name(s):Jennifer Müller
EMail:--email address not disclosed on the web
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Jennifer Müller/MPI-INF, 02/27/2019 02:18 PM
Last modified:
Uwe Brahm/MPII/DE, 03/13/2019 07:01 AM
  • Jennifer Müller, 02/27/2019 02:19 PM -- Created document.