Keep in mind that there is a very mathematical foundation to all this, we're not just tracing paths for the fun of it. Basically what we want to solve is a path integral (an integral over all paths), we do this using a technique called Monte Carlo integration (which basically means we use randomness). We first sample a path (using path tracing) and then we calculate the contribution of that path (which basically is the amount of radiance is carries divided by the probability of sampling the path) and then we add that contribution to the right pixel.
For instance: a beam hits some material and needs to reflect or worse, pass through via transparency. Another issue: If we are calculating on a per pixel basis, that means bundling multiple paths together to figure out what the weighted return will look like. How can this all be computed with any kind of efficiency without cheating?
- Trace random paths from light sources until they terminate (usually decided with Russian roulette).
- Trace random paths from the camera (usually N per pixel, or you can use more paths in noisy areas) until they terminate.
- Try to connect each point in a camera path with each point in a light path, using a simple line test. If it succeeds, that color is added to the pixel from which the camera path originated.
At least that's my understanding; I've only implemented simpler algorithms and read a bit about bidirectional path tracing.
> If we are calculating on a per pixel basis, that means bundling multiple paths together to figure out what the weighted return will look like. How can this all be computed with any kind of efficiency without cheating?
Right, we still need to consider many paths per pixel to get a high quality image. But it converges faster than most other Monte Carlo techniques.
If the surface is refractive/reflective, recursively shoot one more ray calculating the right direction, with correctly diminished intensity and follow the same process.