Understanding the Monte Carlo method for direct lighting estimation is quite fascinating yet complex. Essentially, it seems like it involves randomly selecting directions to simulate light interactions on surfaces and then averaging those effects to predict brightness. This approach has me wondering about its efficiency, especially in detailed scenes. Is uniform sampling is the most effective strategy or are there more refined methods to reduce computational efforts without compromising accuracy?
ninjab3381
There is also a Monte Carlo method titled importance sampling which samples more in areas that are of greater importance to the final result reducing variance.
Mehvix
I'm wondering how MC goes about sampling ωi; perhaps weighing light sources to decrease the chance of misses, or some adaptive algo
Understanding the Monte Carlo method for direct lighting estimation is quite fascinating yet complex. Essentially, it seems like it involves randomly selecting directions to simulate light interactions on surfaces and then averaging those effects to predict brightness. This approach has me wondering about its efficiency, especially in detailed scenes. Is uniform sampling is the most effective strategy or are there more refined methods to reduce computational efforts without compromising accuracy?
There is also a Monte Carlo method titled importance sampling which samples more in areas that are of greater importance to the final result reducing variance.
I'm wondering how MC goes about sampling ωi; perhaps weighing light sources to decrease the chance of misses, or some adaptive algo