Octane AI denoiser - initial tests, results and next steps

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Re: Octane AI denoiser - initial tests, results and next steps

Postby Goldorak » Wed Jul 19, 2017 3:27 pm

Goldorak Wed Jul 19, 2017 3:27 pm
3dworks wrote:can't wait for its release, hopefully it can be integrated well into plugins? will it work with networked GPU's?


We plan to just make it part of Octane itself (both local/cloud) once it's done. It will work through the Octane imager module system, which will include 1st party modules like this one shipping in all OR plug-ins.
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Re: Octane AI denoiser - initial tests, results and next steps

Postby pegot » Wed Jul 19, 2017 7:36 pm

pegot Wed Jul 19, 2017 7:36 pm
Is that a different denoiser than what we currently have or an enhancement to the existing system?
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Re: Octane AI denoiser - initial tests, results and next steps

Postby Goldorak » Wed Jul 19, 2017 9:43 pm

Goldorak Wed Jul 19, 2017 9:43 pm
pegot wrote:Is that a different denoiser than what we currently have or an enhancement to the existing system?


Totally novel. Used machine learning.
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Re: Octane AI denoiser - initial tests, results and next steps

Postby calus » Fri Jul 21, 2017 2:30 pm

calus Fri Jul 21, 2017 2:30 pm
Goldorak wrote:We plan to just make it part of Octane itself (both local/cloud) once it's done. It will work through the Octane imager module system, which will include 1st party modules like this one shipping in all OR plug-ins.


Did Octane devs also investigated on the AI driven Adaptive sampling ?
Post-process denoising is not good for all production.
Can't the AI denoising feature also be used to produce a better noise map to drive adaptive sampling (instead of post process denoising) ?
It could bring to Octane better adaptive sampling a lot more efficient and less user input dependent...

Maybe the ultimate solution is to combine both AI driven Adaptive sampling and AI de-noising.
It seems to be the way Nvidia's Ai denoiser was implemented in IRAY:
"This isn’t just for interactive rendering at artist workstations, as a fully converged noiseless final frame renders is about four times faster; if a final render took an hour before, with the new AI it will take approximately 15 minutes."
https://www.fxphd.com/blog/whats-the-st ... w-iray-ai/
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Re: Octane AI denoiser - initial tests, results and next steps

Postby Goldorak » Sat Jul 22, 2017 2:38 am

Goldorak Sat Jul 22, 2017 2:38 am
calus wrote:
Goldorak wrote:We plan to just make it part of Octane itself (both local/cloud) once it's done. It will work through the Octane imager module system, which will include 1st party modules like this one shipping in all OR plug-ins.


Did Octane devs also investigated on the AI driven Adaptive sampling ?
Post-process denoising is not good for all production.
Can't the AI denoising feature also be used to produce a better noise map to drive adaptive sampling (instead of post process denoising) ?
It could bring to Octane better adaptive sampling a lot more efficient and less user input dependent...

Maybe the ultimate solution is to combine both AI driven Adaptive sampling and AI de-noising.
It seems to be the way Nvidia's Ai denoiser was implemented in IRAY:
"This isn’t just for interactive rendering at artist workstations, as a fully converged noiseless final frame renders is about four times faster; if a final render took an hour before, with the new AI it will take approximately 15 minutes."
https://www.fxphd.com/blog/whats-the-st ... w-iray-ai/


We're choosing to start with AI denoising in this way first for several reasons, but it is all possible on top of this and we're looking into all of it.
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Re: Octane AI denoiser - initial tests, results and next steps

Postby swipswap » Tue Aug 22, 2017 12:48 pm

swipswap Tue Aug 22, 2017 12:48 pm
Will this have an increased rendering speed on motion blurred areas or areas with heavy light scattering?
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Re: Octane AI denoiser - initial tests, results and next steps

Postby Goldorak » Wed Aug 23, 2017 7:34 am

Goldorak Wed Aug 23, 2017 7:34 am
swipswap wrote:Will this have an increased rendering speed on motion blurred areas or areas with heavy light scattering?


We are training it on DOF, MB and more, but it's very hard to measure real world success until we release a test build and see what user feedback tells us in aggregate (tuning will continue for many months after that I would expect).

So far it's doing well on random scenes we throw at it (10 - 50 samples) which are not in way related to the training samples. This is a critical test that has been passed. We're probably over the 50% mark towards a preliminary experimental release which users can help validate further.
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