I don't know too much about it, other than that a symplectic geometer friend helped out, but light field networks seem interesting: https://www.vincentsitzmann.

I don't know too much about it, other than that a symplectic geometer friend helped out, but light field networks seem interesting: https://www.vincentsitzmann.com/lfns/ https://www.vincentsitzmann.com/lfns/ Something I've thought would be interesting is simulating the entire light field by some kind of finite element analysis, but where each element uses a neural network to more quickly converge to the correct lightfield -- and, perhaps like that paper, to use neural networks to give a compact representation of the lightfield at that element. The stratospheric view of what a raytracer even is is a monte carlo sampler to evaluate a (fairly complicated) integral. It seems like many tricks for raytracing are about finding better ways to estimate good sampling distributions (for example, when scattering, a heuristic is to send more rays toward light sources). So, being able to simulate the light field more accurately and quicker could get the rendering integral to converge quicker, too. It sort of reminds me of game-playing AIs like AlphaGo. There's a tree search: the AI is playing a game where a ray is bouncing in a scene, and whenever the beam is scattering it gets to choose which direction to send it -- its goal is to collect light that gives close to the correct pixel color with the least number of beams.