Many certain resources about raytracing tells about:
"shoot rays, find the first obstacle to cut it"
"shoot secondary rays..."
"or, do it reverse and approximate/interpolate"
I didnt see any algortihm that uses a diffusion algorithm. Lets assume a point-light is a point that has more density than other cells(all space is divided into cells), every step/iteration of lighting/tracing makes that source point to diffuse into neighbours using a velocity field and than their neighbours and continues like that. After some satisfactory iterations(such as 30-40 iterations), the density info of each cell is used for enlightment of objects in that cell.
Point light and velocity field:
But it has to be a like 1000x1000x1000 size and this would take too much time and memory to compute. Maybe just computing 10x10x10 and when finding an obstacle, partitioning that area to 100x100x100(in a dynamic kd-tree fashion) can help generating lighting/shadows for acceptable resolution? Especially for vertex-based illumination rather than triangle.
Has anyone tried this approach?
Note: Velocity field is here to make light diffuse to outwards mostly(not %100 but %99 to have some global illumination). Finite-element-method can make this embarassingly-parallel.
Edit: any object that is hit by a positive-density will be an obstacle to generate a new velocity field around the surface of it. So light cannot go through that object but can be mirrored to another direction.(if it is a lens object than light diffuse harder through it) So the reflection of light can affect other objects with a higher iteration limit
Same kd-tree can be used in object-collision algorithms :)
Just to take as a grain of salt: a neural-network can be trained for advection&diffusion in a 30x30x30 grid and that can be used in a "gpu(opencl/cuda)-->neural-network ---> finite element method --->shadows" way.