CodeItNow

In my previous post, I wrote very briefly about an  important improvement to the irradiance caching algorithm – irradiance gradients – and I’m going to expand on rotational gradients this time.

Gradients

The gradient of a function represents both the direction and rate of change of that function as the inputs vary. For a one dimensional function this is simply the derivative of the function. As you move into higher dimensions, you need to consider which coordinate system the inputs for the function are specified in, as this will change how you need to calculate the gradient.

For now, I’m just going to focus on calculating the gradient of a function defined using normalized spherical coordinates. Unfortunately, there’s no real standard way to define spherical coordinates, and despite similar looking symbols, the values are often interchanged. I’m going to define the spherical coordinates on the unit sphere as azimuthal value φ [0, π), and polar value θ [0, 2π).

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Solving the rendering equation with even just one bounce of indirect lighting can take a long time. The majority of time spent rendering a frame is in estimating the lighting integral. For example, rendering a single bounce of indirect lighting at 720p resolution with 256 sample rays for a Monte Carlo estimator requires about 237 million rays to be cast. This doesn’t even include the rays needed for sampling the lights for direct lighting, so in practice, the total will be even higher.

One interesting observation made by Greg Ward in his Siggraph ’88 paper is that contrary to direct lighting, where shadows and lights can cause harsh changes, the indirect lighting on a surface tends to vary relatively slowly. One way to picture why this is, is to imagine the computing average color from the what you can see from each of your eyes. Even though each eye has a slightly different view on the world, the images they see are nearly similar, and so the average color is also nearly the same.

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