Done, thank you so much for the info!

And it's a fantastic coincidence that I'm already next to Vancouver and don't need to suffer from jet lag the 2nd time... a horrible thing

Session's time and location for your schedule:
s2025.conference-schedule.org/presentation...

Currently, the issue with efficient spatial encoding is more or less resolved (iNGP or the new GATE by @boksajak.bsky.social )

But it's not the case for the directional domain at all. Despite its importance for the unbiased rendering, Cache-Based Resampling and 2-Level Monte-Carlo Estimator

To be completely honest, it's a equal-time-comparison:

3SPP vs 1SPP+25 Neural Resamples

I believe we should invest more resources in more rapid adaptivity of neural caches and more aggressive quantizations, so we could deliver it to production real-time rendering

Hmmm, 1 min sounds amazing, I didn't expect such speed!

Congrats to you and to the whole team!

I've got a question: basically the volumetric blocks don't have any transparency, do they? And you rely on pruning to make them look right because of it?

Thanks 🥳

Huge congrats, Valentin!

100% Deserved

If you've missed the paper, just check it out:
mishok43.github.io/nirc/

And feel free to reach out to me, if you wanna help me with pushing forward the neural rendering for real-time applications

That's what I mean by the lack of compute

And of course, huge thanks to my amazing co-authors — Dmitrii Klepikov, Johannes Hanika, Carsten Dachsbacher — and industry friends @kaplanyan.bsky.social , Sebastian Herholz, @momentsingraphics.bsky.social, who helped me on the way!

But there are so many other cool questions that we tried to cover

So please check out our webpage, demo videos, and paper itself: mishok43.github.io/nirc/

📍I’ll be at Eurographics next week in London — if you're around and want to talk rendering, AI, or just grab coffee, DM me!

In equal-time comparisons, NIRC achieves surprisingly cool results both in the biased and unbiased cases

But yeah... variance may increase next to foliage, brush, trees🌿 — still the eternal pain in CG 😅

Using Two-Level Monte Carlo, we can debias NIRC while still cutting variance — thanks to fast cache sampling - dozens of times for the cost of 1 real path

It works like (N)CV, but doesn't introduce any architectural constraints! No need to train Normalizing Flows on-the-fly