Intel Open Image Denoise: High-Performance Denoising Library for Ray Tracing | Hacker News Reader

Intel Open Image Denoise: High-Performance Denoising Library for Ray Tracing

openimagedenoise.github.io

85 pointsingve31 comments

In a similar vein to de-noising realtime path tracing, Christoph Shied has an interesting paper that describes an algorithm that steps around the problem: https://cg.ivd.kit.edu/atf.php

and his Quake2 demos (which are amazing) have gotten a lot of attention on youtube recently: https://www.youtube.com/channel/UCdyHpbF9SgV8uMIWu3oHgdg/vid...

I sometimes wonder if it would be possible for CPUs to have, in addition to their full speed cores, a small part dedicated to parallel tasks with drastically lower speed, but a very high number of cores. Like a tiny graphics card, but with an x86 architecture and less driver problems.

So... Xeon Phi?

Something like that, but with more cores and cheaper.

It looks like there are up to 64 cores in Xeon Phi at 1.5Ghz (price > $1000). A Nvidia 1030 is still better with more than 300 cuda cores at 1.4GHz (price ~= $150). Not that I think that cuda cores and Xeon Phi cores can be compared, but 64 is not that much at that price... What we'd need is dumber cores in very high number. We don't need all fancy things that normal CPU cores have, like virtualization.

Note that Nvidia's core counts are misleading. To apply Nvidia's logic to Xeon Phi, a 64-core Xeon Phi is really 512 cuda cores--each SIMD lane is counted as an independent "Cuda core".

1024 (64 x 16-wide float32).

Good point, I did not know what SIMD was. (Very interesting side projects to come...)

I wish we could talk about GPUs and similar architectures in terms of their total FPU width—how many 32b or 64b floating point operations can be done per clock— and as a secondary measure the number of independent instruction streams that can be worked on in parallel. One provides a upper bound on the arithmetic power of the chip and the other predicts how easy it will be to get close to full utilization of those compute resources. Distilling an architecture down to just one core count only guarantees that you can't compare it against dissimilar architectures.

The Larrabee architecture was something like this, no?

https://www.rs-online.com/designspark/hands-on-with-a-144-co...

Integrated gpus are almost exactly that, minus the x86 part. A very high number of x86 cores would take a ton of chips space and generate a lot of heat.

Or Intel could just unify AVX-512 and Gen machine code.

I don't think it's likely, but I wouldn't be overly surprised if they end up doing something like that.

FPGA pcie card sounds like what you want, I'm not sure if it would have less driver problems though.

https://hackaday.com/2018/02/17/catching-the-pcie-bus/

I've wondered the reverse, why GPU manufacturers don't replace the whole current motherboard, CPU and memory setup and create their own all in 1 PC that runs on the graphics card.

The Larabee were to have 48 Pentium cores, each with their own L2 cache.

https://en.wikipedia.org/wiki/Larrabee_(microarchitecture)

Are these algorithms applicable to low light photography?

Approach yes, exact algorithms no.

What is the difference that makes the algorithm not be applicable? Is it that path tracing noise is "pure monte carlo noise" (I'm sure that has a better term) so has nicer properties from photographic sensor noise (thermal noise with various patterns etc)?

There are two huge factors, the first even more potent than the second.

First, there is much more information per sample and per pixel. Normals, depth, separate diffuse color, separate diffuse texture, first bounce normal, first bounce position, etc. There is a technique that just uses color, but uses histograms of all the samples within a pixel.

Second, for a video game you can accumulate samples temporally. Many of these demos will use one sample per pixel, but filtering information can be kept on every frame and reused to some degree.

Oh I thought this operated on a 2D hdr image, without further info (I admit I didn’t read the details).

If it can’t be used on a still I rendered 10 years ago with code that isn’t around anymore then it’s clear it can be used on a photo either.

This is trained on monte carlo based rendering noise, and optionally uses additional available rendering data like albedo and normal passes. So while the idea of using deep learning for noise reduction in general works, this codebase in particular isn't suitable for that task.

One issue is also building large enough training data set for clean vs noisy images. Approaches like https://arxiv.org/abs/1803.04189 try to work around that

Other approaches (like Google's pixel cameras and astrophotography) additionally take high speed burst photos and average pixels to reduce noise, this can be used together with deep learning based noise reduction (or other noise reduction techniques) for better results

For the example picture, it would be nice to have it also compared to a fully raytraced image. I'd love to know how many rays it would take to be equivalent to the denoised version.

Blender has a denoiser, but this looks def better. I wonder if it can be integrated? Also, does this also work for AMD?

I'm working on it: https://twitter.com/stefan_3d/status/1090607219975045120

It should work on any CPU with SSE4.2 support, which includes all modern AMD CPUs.

Intel's graphics libraries are well crafted and pleasant to integrate. In fact, Embree is currently being integrated into Blender's Cycles as an alternative ray engine.

I'd guess we'll see an effort at integrating the Intel denoiser fairly soon as a plugin at least.

Someone recently released a Blender plugin using the NVIDIA Optix denoiser, which I think couldn't be directly integrated due to licensing issues:

https://remingtongraphics.net/tools/d-noise/

Slightly related, came across this addon[0] that allows you to use NVIDIA OptiX Denoiser directly from Blender.

[0] https://github.com/troopy28/OptiX-Denoiser-for-Blender

How does it work? Some of the examples (especially the crytek sponza one) seem impossible.

Off topic: are there similar efforts for denoising audio? Any pointers appreciated.

Look at the Noise Reduction plugin in Sound Forge or a similar audio editor.

yes, this is an active area of study. You could check out https://people.xiph.org/~jm/demo/rnnoise/

Sorry for the late reply, already tried it, not so good results.

Reply on news.ycombinator.com