[Bf-committers] [GSoC] Cycles Denoiser

Tue Mar 22 16:43:55 CET 2016

I didn't think about it, but it shouldn't be a problem creating a
stand-alone binary.

On Tue, Mar 22, 2016 at 4:15 PM, François T. <francoistarlier at gmail.com> wrote:
> 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
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