Improved half vector space light transport and Manifold NEE

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Paleos
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Joined: Tue Apr 08, 2014 1:32 am

Improved half vector space light transport and Manifold NEE

Postby Paleos » Sun May 31, 2015 4:34 am

I came across two papers on the website for Johannes Hanika, that are promising

Improved half vector space light transport

Abstract:
"In this paper, we present improvements to half vector space light transport (HSLT) [KHD14], which make this
approach more practical, robust for difficult input geometry, and faster. Our first contribution is the computation
of half vector space ray differentials in a different domain than the original work. This enables a more uniform
stratification over the image plane during Markov chain exploration. Furthermore, we introduce a new multi
chain perturbation in half vector space, which, if combined appropriately with half vector perturbation, makes the
mutation strategy both more robust to geometric configurations with fine displacements and faster due to reduced
number of ray casts. We provide and analyze the results of improved HSLT and discuss possible applications of
our new half vector ray differentials."

Manifold next event estimation

This paper describes an idea with similarities to what I suggested in http://ompf2.com/viewtopic.php?f=3&t=2031

Abstract:
"We present manifold next event estimation (MNEE), a specialised technique for Monte Carlo light transport
simulation to render refractive caustics by connecting surfaces to light sources (next event estimation) across
transmissive interfaces. We employ correlated sampling by means of a perturbation strategy to explore all half
vectors in the case of rough transmission while remaining outside of the context of Markov chain Monte Carlo,
improving temporal stability. MNEE builds on differential geometry and manifold walks. It is very lightweight in
its memory requirements, as it does not use light caching methods such as photon maps or importance sampling
records. The method integrates seamlessly with existing Monte Carlo estimators via multiple importance sampling."

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