[Bf-committers] [GSoC] Cycles Denoiser

Tue Mar 22 16:15:57 CET 2016

Are you thinking of having denoiser as an extra binary like for Renderman ?
So it could be use with other renderer and/or outside of blender as well ?

2016-03-22 15:46 GMT+01:00 Fabrizio Destro <destro.fabrizio at gmail.com>:

> Thank you for your feedback. I am going to read them.
> I am currently working on my proposal, all suggestions are welcome
>
>
> https://docs.google.com/document/d/18UkmWPSoMutiJaxr5mXDkkB4ccyqu0nC19xVb2qdq-0/edit?usp=sharing
>
>
> On Tue, Mar 22, 2016 at 11:16 AM, Sergey Sharybin <sergey.vfx at gmail.com>
> wrote:
> >
> > Hi,
> >
> > General idea of denoisers is to blur noisy areas. Now, how to detect if
> the
> > area is noisy or not? There are several approaches to this: it could be
> > image-space variance based approach or it could be an approach based on
> > per-pixel variance. The later one seemed to be more promising from own
> > experiments. Additionally, you should not blur background with foreground
> > (roughly speaking) and you'll need to have a way to distinguish areas
> which
> > could be blurred together and which are not. It could be based on depth,
> > normal, UV coordinate and so on. All this extra information requires
> extra
> > memory.. That's what wiki meant basically.
> >
> > I think we'll indeed need to have some sort of "framework" for denoising,
> > so we'll be able to have quick viewport previews with more aggressive
> > algorithms (which are usually not so much temporary stable and will cause
> > low frequency noise in the animation) and we'll be able to have a less
> > aggressive denoiser to simply get rid of last bit of MC noise.
> >
> > Can't speak of exact milestones, that's something dependent on your exact
> > proposal, skills and such..
> >
> > You might want to have a look into following papers:
> >
> > - Filtering and Blending of High-Variance Light Paths with Perceptual
> > Control, Karsten Schwenk
> > - Guided image filtering, Kaiming He et al.
> > - Recent Advantages in Adaptive Sampling and Reconstruction for Monte
> Carlo
> > Rendering, M. Zwicker et al.
> > - Removing the noise in Monte Carlo Rendering with General Image
> Denoising
> > Algorithms, Nima Khademi Kalantari and Pradeep Sen
> >
> > (should be easy to find links, i only have those papers printed, no links
> > handy)
> >
> > There were also some interesting presentation at the SIGGRAPH 2015, you
> can
> > find some notes and papers titles there:
> >
> http://s2015.siggraph.org/attendees/courses/events/denoising-your-monte-carlo-renders-recent-advances-image-space-adaptive
> >
> >
> > On Mon, Mar 21, 2016 at 11:40 AM, Fabrizio Destro <
> destro.fabrizio at gmail.com
> > > wrote:
> >
> > > Hi, thank you for the materials.
> > >
> > > I've just read "Path-space motion estimation [...]" and for what I've
> > > understood they use different tools to reduce the noise on the image:
> > > Decompositions, Motion estimations of reflections and other effects,
> > > Denoising, Spatial and Temporal upsampling. The first milestone could
> > > be to think about a generic framework for denoising in terms of
> > > interfaces and modules, and start implementing a 'skeleton'.
> > >
> > > In their work they cited this research "On Filtering the Noise from
> > > the Random Parameters in Monte Carlo Rendering", on this paper they
> > > talk about a method to reduce the noise which works in image space.
> > > Maybe in a possible schedule the second milestone could be an
> > > implementation of denoiser like that, which works only on the image.
> > >
> > > After these two milestone have been delivered, the next step on the
> > > schedule will be the implementation of the modules inside this
> > > framework (Motions estimations of relfections, etc...)
> > >
> > >
> > > On Sun, Mar 20, 2016 at 8:10 PM, François T. <
> francoistarlier at gmail.com>
> > > wrote:
> > > > Hello,
> > > >
> > > > Disney has several recent research on the subject...
> > > >
> > > > https://www.disneyresearch.com/publication/pathspace-decomposition/
> > > >
> > > >
> > >
> https://www.researchgate.net/publication/281678889_Boosting_Histogram-Based_Denoising_Methods_with_GPU_Optimizations
> > > >
> > > >
> > > >
> > > > 2016-03-20 19:24 GMT+01:00 Fabrizio Destro <
> destro.fabrizio at gmail.com>:
> > > >
> > > >> Hello everybody! I am Fabrizio I always wanted to contribute to an
> > > >> Open Source project. I found out about GSoC about three years ago,
> but
> > > >> I have never applied because I wouldn't have had the time. But, this
> > > >> year I would like to try.
> > > >>
> > > >> I have looked through the proposed ideas and some of them catch my
> > > >> attention. In particularly the Cycles denoiser, I have some
> questions
> > > >> about it.
> > > >>
> > > >> First, I want to be sure I understand what the goal is. So, the
> > > >> objective is to create a node which, once the rendering is done,
> will
> > > >> work only on the image with the goal to reduce the noise.
> > > >>
> > > >> I am not sure about this sentence I found on the wiki: "[...] and
> > > >> requires a special buffer with 'delta' information for speed, UV
> > > >> [...]". This means that this node will store some data to speed up
> the
> > > >> process on the next rendering? and if so, these data will be valid
> > > >> only if the scene didn't change from the last time, right?
> > > >>
> > > >> I am currently doing some research online and I found this
> publication
> > > >> on the subject http://dl.acm.org/citation.cfm?doid=2776880.2792740
> .
> > > >> Does someone have any reference which can be useful? or maybe an
> idea
> > > >> on some algorithms/researches?
> > > >> _______________________________________________
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> > > >>
> > > >
> > > >
> > > >
> > > > --
> > > > ____________________
> > > > François Tarlier
> > > > www.francois-tarlier.com
> > > > www.linkedin.com/in/francoistarlier
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> > >
> >
> >
> >
> > --
> > With best regards, Sergey Sharybin
> > _______________________________________________
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-- 
____________________
François Tarlier
www.francois-tarlier.com
www.linkedin.com/in/francoistarlier