Lecture 13: Global Illumination & Path Tracing (23)
rishiskhare
What is H^2 - i.e. what are we integrating over. It seems that it's over dwi, but is this across the hemisphere?
Edge7481
@rishikhare H^2 is the hemisphere above the surface where light can come from. It is essentially calculating the reflected light from all possible incident directions
ShivanPatel2025
How does the choice of p(w) for sampling directions in the Monte Carlo estimate affect solving the reflection equation
dhruvchowdhary
@ShivanPatel2025 The choice of p(ω) affects the efficiency and accuracy of solving the reflection equation. Choosing a distribution that closely matches the BRDF or the light distribution, reduces the number of samples needed for a high quality approximation. This minimizes variance, leading to a more accurate rendering.
tiffanysun1
The Monte Carlo estimation mentioned here seems like an efficient way to approximate the integral for real-time rendering. However, I'm curious about the computational cost associated with generating a large number of sample directions ω_j. It appears to be a trade-off between accuracy and performance. I'm also wondering how different choices for the probability distribution p(ω) would affect the visual outcome and whether there's a method to optimize the balance between quality and computation time, especially for complex scenes.
What is H^2 - i.e. what are we integrating over. It seems that it's over dwi, but is this across the hemisphere?
@rishikhare H^2 is the hemisphere above the surface where light can come from. It is essentially calculating the reflected light from all possible incident directions
How does the choice of p(w) for sampling directions in the Monte Carlo estimate affect solving the reflection equation
@ShivanPatel2025 The choice of p(ω) affects the efficiency and accuracy of solving the reflection equation. Choosing a distribution that closely matches the BRDF or the light distribution, reduces the number of samples needed for a high quality approximation. This minimizes variance, leading to a more accurate rendering.
The Monte Carlo estimation mentioned here seems like an efficient way to approximate the integral for real-time rendering. However, I'm curious about the computational cost associated with generating a large number of sample directions ω_j. It appears to be a trade-off between accuracy and performance. I'm also wondering how different choices for the probability distribution p(ω) would affect the visual outcome and whether there's a method to optimize the balance between quality and computation time, especially for complex scenes.