❤️ Check out Weights & Biases and say hi in their community forum here: https://wandb.me/paperforum
📝 The paper "Temporally Stable Real-Time Joint Neural Denoising and Supersampling" is available here:
https://www.intel.com/content/www/us/en/developer/articles/technical/temporally-stable-denoising-and-supersampling.html
📝 Our earlier paper with the spheres scene that took 3 weeks:
https://users.cg.tuwien.ac.at/zsolnai/gfx/adaptive_metropolis/
❤️ Watch these videos in early access on our Patreon page or join us here on YouTube:
- https://www.patreon.com/TwoMinutePapers
- https://www.youtube.com/channel/UCbfYPyITQ-7l4upoX8nvctg/join
🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible:
Aleksandr Mashrabov, Alex Balfanz, Alex Haro, Andrew Melnychuk, Benji Rabhan, Bryan Learn, B Shang, Christian Ahlin, Eric Martel, Geronimo Moralez, Gordon Child, Jace O'Brien, Jack Lukic, John Le, Jonas, Jonathan, Kenneth Davis, Klaus Busse, Kyle Davis, Lorin Atzberger, Lukas Biewald, Luke Dominique Warner, Matthew Allen Fisher, Matthew Valle, Michael Albrecht, Michael Tedder, Nevin Spoljaric, Nikhil Velpanur, Owen Campbell-Moore, Owen Skarpness, Rajarshi Nigam, Ramsey Elbasheer, Steef, Taras Bobrovytsky, Ted Johnson, Thomas Krcmar, Timothy Sum Hon Mun, Torsten Reil, Tybie Fitzhugh, Ueli Gallizzi.
If you wish to appear here or pick up other perks, click here: https://www.patreon.com/TwoMinutePapers
Thumbnail background design: Felícia Zsolnai-Fehér - http://felicia.hu
Károly Zsolnai-Fehér's links:
Instagram: https://www.instagram.com/twominutepapers/
Twitter: https://twitter.com/twominutepapers
Web: https://cg.tuwien.ac.at/~zsolnai/
Оглавление (2 сегментов)
Segment 1 (00:00 - 05:00)
Dear Fellow Scholars, this is Two Minute Papers with Dr. Károly Zsolnai-Fehér. This is my happy episode. Why is that? Well, of course, because today we are talking about light transport simulations, and in particular, Intel’s amazing new technique, that can take this, and make it into…this! Wow. It can also take this, and make it into…this. My goodness, this is amazing. But wait a second, what is going on here? What are these noisy videos for, and why? Well, if we wish to create a truly gorgeous photorealistic scene, in computer graphics, we usually reach out to a light transport simulation algorithm, and then, this happens. Oh no! We have noise. Tons of it. But why? Well, during the simulation, 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 rays, the inaccuracies in our estimations show up as noise in these images. This clears up over time, but it may take a long time. How do we know that? Well, have a look at the reference simulation footage for this paper. See? There is still some noise in here. I am sure this should clean up over time, but no one said that it would do so quickly. A video like this might require hours to days to compute. For instance, this is from our previous paper that took 3 weeks to finish and it ran on multiple computers at the same time. So, is all hope lost for these beautiful photorealistic simulations? Well, not quite! Instead of waiting for hours or days, what if I told you that we can just wait for a small fraction of a second, about 10 milliseconds. It will produce this. And then, run a previous noise filtering technique that is specifically tailored for light transport simulations, and what do we get? Probably not much, right? I can barely tell what I should be seeing here. So, let’s see a previous method. Whoa! That is way better. I was barely able to guess what these are, but now we know: gratings. Great! So, we don’t have to wait for hours to days for a simulated world to come alive in a video like this, just a few milliseconds. At least for the simulation, we don’t know how long the noise filtering takes. And now, hold on to your papers, because this was not today’s paper’s result. I hope this one can do even better. And, look, instead, it can do this. Wow. This is so much better! And the result of the reference simulation for comparison, this is the one that takes forever to compute. Let’s also have a look at the videos and compare them. This is the noisy input simulation, wow, this is going to be hard. Now, the previous method. Yes, this is clearly better, but there is a problem. Do you see the problem? Oh yes, it smoothed out the noise, but it smoothed the details too. Hence, a lot of them are lost. So, let’s see what Intel’s new method can do instead. Now we’re talking! So much better. I absolutely love it. It is still not as sharp as the reference simulation, however, in some regions, depending on your taste, it might even be more pleasing to the eye than this reference. And it gets better! This technique does not only denoising, but upsampling too. This means that it is able to create a higher resolution image with more pixels than the input footage. Now, get ready, one more comparison and I’ll tell you how long the noise filtering took. Whoa, I wonder what it will do with this noisy mess. I have no idea what is going on here. And neither does this previous technique. And this is not some ancient technique, this previous method is the Neural Bilateral Grid, a learning-based method from just two years ago. And now, have a look at this. My goodness! Is this really possible? So much progress just one more paper down the line! I absolutely love it. So good!
Segment 2 (05:00 - 06:00)
So, how long do we have to wait for an image like this? Still hours to days? Well, not at all. This all runs not only in real time, it runs faster than real time! Yes, that means about 200 frames per second for the new noise filtering step. And remember, the light simulation part typically takes 4-12 milliseconds on these scenes, this is the noisy mess that we get. And just 5 milliseconds later, we get this. I cannot believe it. Bravo! So, real-time light transport simulations from now on? Oh yes, sign me up right now! What a time to be alive! So, what do you think? Let me know in the comments below! Thanks for watching and for your generous support, and I'll see you next time!