2 HN users recommended this explainer2 months2018–2019score 2
Years with an HN recommendation, 2007–2026
Cited as an accessible explanation of AI denoising, which underpins real-time ray tracing on RTX hardware.
A Two Minute Papers episode on NVIDIA's recurrent denoising autoencoder, which cleans up noisy Monte Carlo path-traced image sequences. It shows how a neural network can produce usable renders from very few samples.
“Two Minute Papers did a nice explanation of it” (HN, 2019)
Disney actually does a lot of research combining the world of graphics with deep learning. Some examples that you might appreciate (from the excellent channel "Two Minute Papers"): . "Disney's AI Learns To Render Clouds" [0] . "AI Learns Noise Filtering For Photorealistic Videos" [1] [0] [link] [1] [this video]
Neural noise filtering for photorealistic rendered video.
It's only briefly mentioned, but NVIDIA's recurrent denoising autoencoder is pretty amazing. [link] Two Minute Papers did a nice explanation of it: [this video]
Nice explanation of NVIDIA's recurrent denoising autoencoder.
Deep learning denoiser. I don't know if NVIDIA came up with some marketing term for it. It's what allows RTX to produce meaningful images at all despite the renderer running at 0.5-2 samples per pixel which looks little better than random noise. I kind of assumed they'd call it DLDN because they called their deep learning supersampling DLSS. I guess not, go figure. [this video]
Shows deep learning denoising that makes low-sample ray tracing usable.
Video list and Hacker News comment labels from HackerTube (prompted by @karpathy), released under ODC-By 1.0, built on the open-index/hacker-news archive. Hacker News data is provided by Y Combinator; comments remain the work of their authors. Video titles, channel names and thumbnails belong to their creators and are shown via YouTube. HackerTube Watch is an unofficial edition. Data export of 2026-10-31.
Plays via YouTube's privacy-enhanced embed
5 Hacker News comments
From the HN archive; up to 20 kept per video, favoring different commenters. Recommendations first.
Citations, criticism and jokes