Noticed a lot of inaccuracies in the article. > It also requires light sources to have actual sizes, a bit of a departure from traditional point light sources that have a position but are treated like an infinitely small point in space You don't wait for the ray to bounce into a light. Lights are typically sampled directly at each bounce point in a path tracer. If you waited for the beam to hit a light it would take…
> You can use Russian roulette techniques Did you mean Monte Carlo?
Noticed a lot of inaccuracies in the article. > It also requires light sources to have actual sizes, a bit of a departure from traditional point light sources that have a position but are treated like an infinitely small point in space You don't wait for the ray to bounce into a light. Lights are typically sampled directly at each bounce point in a path tracer. If you waited for the beam to hit a light it would take…
> You can use Russian roulette techniques Did you mean Monte Carlo?
Russian roulette often refers to when a Ray has a random chance of terminating (being absorbed)
I wonder what convolutional neural networks bring to the table. Maybe you could use them to get rid of the noise, or then you could use ray tracing for the base image and train a CNN to "recolour" that image based on a noisy path traced one of the same scene. This demo is 4 years old, but the noise still seems to be a problem: https://youtu.be/BpT6MkCeP7Y If anybody's got more recent impressive demos to link, I'd lik…
This uses a neural network https://www.youtube.com/watch?v=YjjTPV2pXY0 video results start around 1:15 This one does not use a neural network: https://www.youtube.com/watch?v=HSmm_vEVs10
Those both only use global motion, which is the simplest case for temporal filtering; it would be interesting to see the effects with local motion (e.g. a blinking light).
AFAIK current state-of-the-art in pathtracing are still metropolis light transport methods. Seems like current research is mostly focussed on denoising - which makes sense, since a good denoising provides a valuable shortcut for quicker results. While I only occasionally glance at new results in this area it seems that CNN-based denoising techniques look quite promising, possibly getting us close to viable real-time…
Metropolis has lots of advantages, but it has serious problems for realtime or animated applications, in that it tends to flicker or have noticeable low-frequency noise. This is less of a problem for stills. RTX can absolutely accelerate path tracers, even for non-realtime applications. The underlying framework is definitely flexible enough to support a variety of rendering algorithms, it's basically accelerated BVH…
This is slightly irrelevant but presumably someone could invent a version of Metropolis that also distributed its rays across time as well as space, thereby ensuring that any bright/flickery pixels would remain coherent across frames; i.e. once that path was discovered on one frame, it could propagate that path information out to previous/succeeding frames.