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For those interested in details:
It is following the approach outlined in sections 7.1.3 and 7.2 of this paper describing RenderMan:
graphics.pixar.com/library/Render…
The error metric is from section 2.1 here: pdfs.semanticscholar.org/8329/759ae51c9…
It is following the approach outlined in sections 7.1.3 and 7.2 of this paper describing RenderMan:
graphics.pixar.com/library/Render…
The error metric is from section 2.1 here: pdfs.semanticscholar.org/8329/759ae51c9…
In layman's terms: Every other sample is written to a separate buffer. By comparing this extra buffer to the main image buffer, the renderer can estimate convergence.
Pixels receive progressively more samples until a convergence threshold or sample count limit is reached.
Pixels receive progressively more samples until a convergence threshold or sample count limit is reached.
Testing @intel #OpenImageDenoise inside #b3d. Not fast enough for real-time on my ancient Ivy Bridge machine.
Quick and dirty hack into the drawing code, not taking advantage of albedo and normal buffers yet.
Quick and dirty hack into the drawing code, not taking advantage of albedo and normal buffers yet.
Turned it into a compositing node. Extra inputs are still missing. This should be useful for denoising animations or using it with third party render engines. 
Classroom scene, animated at 64spp. Admittedly, 64spp is extremely low and I can't expect a static image denoiser to handle that without artifacts.
I hope OpenImageDenoise gets temporal denoising in the future.
I hope OpenImageDenoise gets temporal denoising in the future.
