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Crowdrender and Denoising in Blender 2.79

June 28, 2017


200 samples at 1080p frame size - glass bsdf shader on plate, diffuse bsdf on plane with checker texture


Initial Frustrations


Wouldn't it be nice if you could render using the denoiser in blender 2.79 and still be able to use multiple machines (for both single frames and sequences) without having artefacts where the tiles meet? Man I sounded like such a salesman there! Sorry :P


If you've never tried bucket rendering for single frames, you might not have experienced the blood boiling frustration of having artefacts in your final render - be they squiggles, fireflies or discontinuities. Getting rid of them is not exactly an exact science even when you're not splitting up a render into tiles. So when we had some artefacts whilst testing the denoiser with crowd render, for us it was certainly a show stopper, at first. I mean really, no denoiser support? Might as well pack up and go home. 



Wait, what is a Crowdrender?


Ahhh, look at me asking questions like I am you reading this article! Sorry, a little too mainstream internet blog, thinking I am a clever clogs. Ahem, I'll get to the point. 

If you haven't heard, crowd render is a network rendering add-on for blender, it allows you to connect many computers together to render stills and sequences. In this article we're exploring how it works with Blender's new (coming in 2.79) denoiser feature.


If you want the short story, it works, you can (in version 0.1.2 of our add-on, as yet unreased, just like 2.79) render frames using multiple computers and use the denoiser to get the same great results in less time. 


If you love a little reading, then read on my fine fellow/fare lady.


Delving a little deeper


So, for those of you who love to read, here is a reference to an academic journal (its a paid one, so unfortunately you only get to read the abstract for free) behind the new denoiser. 




If you like seeing results rather than indulging in theory, you can see the Adaptive Rendering based on Weighted Local Regression in action in a video presentation at the link below.




Anyway, the denoiser basically works by doing a lot of data analysis of data in the image plane, thats pixel data as far as my limited understanding goes. The theory goes that you can sample the data in the image and remove noise and keep detail. We know this works cause, well look at the image, it convinced me (the video above is also amazing by the way). 

The controls in blender give a hint at how it works, think of it as a very advanced blur, you get to control things like the radius and strength of the blur. The radius restricts what pixel data at any given location in the image is used to remove noise. The strength affects how strongly the data is weighted. Basic enough explanation, I guess. 


In the image below you can see a render where we intentionally disabled denoising on one machine so you can see the difference. This image was rendered with 200 samples, you can see the clarity of the denoised image is quite striking. 



 The catch, with multi-tile rendering


We've come across a similar issue with compositing tiles coming from separate computers or processes. Initially we'd tried compositing each tile on the computers we were using as opposed to sending all the tiles back to the user's main computer (sometimes called a client). This resulted in artefacts along the edges of each tile (like the image below). Wherever two tiles meet, the result computed for each tile is different as it is missing some data from the other tiles it shares an edge with. So we had to send the tiles un-composited back to the client where they were compiled into a single image and then sent through the compositor. 


In the image below, initially we had the same issue as with compositing, there were artefacts. Though in this image, they're highly accentuated. What you are actually looking at is not a final render but a result from Blender's compositor. We simply took the images from two computers and then put them into a color mix node set to "difference". 


What this does is to render pixels that are identical in overlapping areas of the two tiles as black. Those pixels which are not identical have a non zero (hence brighter) colour value. You can clearly see where the two tiles have been overlapped. Now we see what the denoiser is up to along the tile boundaries. Its a little hard to see but from this image you can see that the areas that overlap are fairly similar since they're dark, but there are brighter areas where the two images are different. 


So, it appears the the sampling used in the denoiser results in a different value for pixels near a boundary or edge of the image frame. Pants. Not what we were hoping for, but logical now we realise that its doing something similar to a blur node, albeit way more sophisticated. 


The solution, how to remove the artefacts without having to hack


Looking a little more closely at the problem, the solution materialised (as it does when you care to really have a good look at something ;P). On closer inspection we could see that the differences in pixel values between the two tiles were less in the centre of the overlap than along the edges. This made sense. If the denoiser is using a radial pattern to sample, then where that radius overlaps an edge, it will be missing data it would otherwise have and result in deviations from what we'd see if there wasn't a join.