NVIDIA Just Supercharged Ray Tracing!
Key Takeaways
The video discusses a new paper on Area ReSTIR, a technique for real-time defocus and antialiasing in ray tracing, and its potential to supercharge ray tracing capabilities, with a collaboration between the University of Utah and NVIDIA.
Full Transcript
Feast Your Eyes Upon these beautiful images oh my goodness and get this none of them are real none yes that's right these images were created through a technique called Ray tracing simulating the path of light rays to create beautiful images and animations and goodness it would be an absolute dream to be able to create video games that look like this so wait why is not everything retraced today well there is a problem and not a small one and this problem is noise look what is going on here well during this process we have to shoot millions and millions of light rays into the scene to estimate how much light is bouncing around and before we have simulated enough our phrase the inaccuracies in our estimations show up as noise in these images don't worry this clears up over time as we simulate more rays but it may take from minutes to days for this to happen even for a smaller scene for instance in an earlier paper this one took us three full weeks to finish three weeks yes really I am not kidding ouch so can we do this in real time well here is a classical technique past tracing trying to create images in quick succession for Real Time applications who this is a terrible disappointment as you see there is unfortunately plenty of noise here but wait noise filtering techniques exist that are specifically designed for L transport and these do an absolutely amazing work look at how much more we know about this scene but still come on if you look at this who would really be able to guess what the real image should be no one now wait a second we have Decades of research works in the area of Ray tracing so if we jump in time about 30 years we get this this is so much better however it is unfortunately still not that usable so is that it is all hope last for getting these beautiful R traced images in real time well don't despair despite the fact that unfortunately the problem gets even worse dear fellow Scholars this is two-minute papers with Dr car how does it get even worse well as soon as you start moving the camera we get this thing that we call high frequency noise that makes almost all of this footage completely unusable even after noise filtering there is just too much flickering and now hold on to your papers fellow scholar because here is an absolutely miraculous new paper that can do this instead this is a collaboration between the University of Utah and Nvidia and it not only looks so much better immediately wow however it has four more huge advantages that only reveal themselves to experience fellow Scholars with a eye one trying to look at the frequency of the noise it is of much lower frequency in other words it is a great deal less jumpy so when we hand it over to the denoiser it can do so much better with it two it also has much better anti-aliasing what does that mean well look the edges are more visible better resolved once again that is a lot more helpful for the denoiser three it is a ble to get more information out of each light path that we simulate how much more well 25 times more yes these results are equivalent to 25 times more Rays with previous techniques that is a huge leap in capabilities and four large glowing light sources that are not in focus called boka seem to be often completely gone with the previous methods for quite a while but are resolved better with this technique I am absolutely stunned by these results so good now despite all this not even this technique is perfect three things here one we don't yet know how well it generalizes to motion blur things getting a little blurry when we move objects or the camera around two if we need a super sharp steel image and we are willing to wait for longer there are some moving Parts called pixel filters that this does not do well with however this is not super relevant for real-time applications and three it takes a tiny bit more time per frame than previous techniques but it is totally worth it and there are some more good news the source code of this project is also available they just give this out to all of us free of charge absolutely amazing when I was a PhD student I was thinking whether realtime R Racing will be a possibility in my lifetime and here we are approximately 10 years later and we get this I can't believe it what a time to be alive make sure to have a look at the paper in the video description it is beautifully written also I have a master level course online transport that is completely free for everyone no strings attached we write a full light simulation program from scratch there check it out if you're looking for inexpensive Cloud gpus for AI Lambda now offers the best prices in the world for GPU Cloud compute no commitments or negotiation required just sign up and launch an instance and hold on to your papers because with the Lambda GPU Cloud you can now get on demand h100 instances and and they are one of the first Cloud providers to offer publicly available OnDemand h100 accs did I mention they also offer persistent storage so join researchers at organizations like apple MIT and ctech in using Lambda Cloud instances workstations or servers make sure to go to lamb.com papers to sign up for one of their amazing GPU instances today
Original Description
❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers
📝 The paper "Area ReSTIR: Resampling for Real-Time Defocus and Antialiasing" is available here:
https://graphics.cs.utah.edu/research/projects/area-restir/
https://github.com/guiqi134/Area-ReSTIR
My free course on ray tracing / light transport:
https://users.cg.tuwien.ac.at/zsolnai/gfx/rendering-course/
Our earlier paper with the spheres scene that took 3 weeks:
https://users.cg.tuwien.ac.at/zsolnai/gfx/adaptive_metropolis/
📝 My paper on simulations that look almost like reality is available for free here:
https://rdcu.be/cWPfD
Or this is the orig. Nature Physics link with clickable citations:
https://www.nature.com/articles/s41567-022-01788-5
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Alex Balfanz, Alex Haro, B Shang, Benji Rabhan, Gaston Ingaramo, Gordon Child, John Le, Kyle Davis, Lukas Biewald, Martin, Michael Albrecht, Michael Tedder, Owen Skarpness, Richard Sundvall, Taras Bobrovytsky, Ted Johnson, Thomas Krcmar, Tybie Fitzhugh, Ueli Gallizzi.
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Thumbnail background design: Felícia Zsolnai-Fehér - http://felicia.hu
Károly Zsolnai-Fehér's research works: https://cg.tuwien.ac.at/~zsolnai/
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