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 <[email protected]> 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 <[email protected] > > 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. <[email protected]> > > 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 <[email protected]>: > > > > > >> 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? > > >> _______________________________________________ > > >> Bf-committers mailing list > > >> [email protected] > > >> http://lists.blender.org/mailman/listinfo/bf-committers > > >> > > > > > > > > > > > > -- > > > ____________________ > > > François Tarlier > > > www.francois-tarlier.com > > > www.linkedin.com/in/francoistarlier > > > _______________________________________________ > > > Bf-committers mailing list > > > [email protected] > > > http://lists.blender.org/mailman/listinfo/bf-committers > > _______________________________________________ > > Bf-committers mailing list > > [email protected] > > http://lists.blender.org/mailman/listinfo/bf-committers > > > > > > -- > With best regards, Sergey Sharybin > _______________________________________________ > Bf-committers mailing list > [email protected] > http://lists.blender.org/mailman/listinfo/bf-committers _______________________________________________ Bf-committers mailing list [email protected] http://lists.blender.org/mailman/listinfo/bf-committers
