POV-Ray : Newsgroups : povray.general : Did you hear from "Gradient-Domain Path Tracing"? Server Time
25 Nov 2024 06:32:35 EST (-0500)
  Did you hear from "Gradient-Domain Path Tracing"? (Message 1 to 2 of 2)  
From: Theogott
Subject: Did you hear from "Gradient-Domain Path Tracing"?
Date: 5 Jan 2016 16:55:00
Message: <web.568c3a8e5be96c8cadb2e4f80@news.povray.org>
This is more new technical algos for Raytracing.

Gradient Domain Path Tracing is an interesting method to get a clean image with
a rather low amount of samples. The implementation has now been made available
for Mitsuba Render.

If you think of implementing some sort of bidirectional Path tracing algo for
POV, take a look.

http://www.cgg.unibe.ch/publications/gradient-domain-bidirectional-path-tracing

We introduce gradient-domain rendering for Monte Carlo image synthesis.

While previous gradient-domain Metropolis Light Transport sought to distribute
more samples in areas of high gradients, we show, in contrast, that estimating
image gradients is also possible using standard (non-Metropolis) Monte Carlo
algorithms, and furthermore, that even without changing the sample distribution,
this often leads to significant error reduction. This broadens the applicability
of gradient rendering considerably. To gain insight into the conditions under
which gradient-domain sampling is beneficial, we present a frequency analysis
that compares Monte Carlo sampling of gradients followed by Poisson
reconstruction to traditional Monte Carlo sampling.

Finally, we describe Gradient-Domain Path Tracing (G-PT), a relatively simple
modification of the standard path tracing algorithm that can yield far superior
results.


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From: Theogott
Subject: Re: Did you hear from "Gradient-Domain Path Tracing"?
Date: 5 Jan 2016 17:10:01
Message: <web.568c3eb72319d779adb2e4f80@news.povray.org>
We introduce gradient-domain rendering for Monte Carlo image synthesis. While
previous gradient-domain Metropolis Light Transport sought to distribute more
samples in areas of high gradients, we show, in contrast, that estimating image
gradients is also possible using standard (non-Metropolis) Monte Carlo
algorithms, and furthermore, that even without changing the sample distribution,
this often leads to significant error reduction. This broadens the applicability
of gradient rendering considerably. To gain insight into the conditions under
which gradient-domain sampling is beneficial, we present a frequency analysis
that compares Monte Carlo sampling of gradients followed by Poisson
reconstruction to traditional Monte Carlo sampling. Finally, we describe
Gradient-Domain Path Tracing (G-PT), a relatively simple modification of the
standard path tracing algorithm that can yield far superior results.

One more link ... on this topic:
https://mediatech.aalto.fi/publications/graphics/GPT/


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