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Overview ¶
CDFs are useful when sampling from distributions. For example, you could create a CDF of light source power. This would allow sampling of lights with a probability based on power. This is an example of a discrete CDF, where sampling selects among a fixed set of probabilities. (See the example below.)
Use the sample_cdf function to sample values from the returned CDF array.
Usage ¶
float [] create_cdf(float pdf[])
Returns a CDF for the input PDF as an array of floats.
pdf
Array of PDF values to create the CDF for.
Examples ¶
// Iterate over all lights, sampling their power int[] li = getlights(); float values[]; resize(values, len(li)); int nsamples = 256; int sid = israytrace ? SID : newsampler(); vector s, pos, clr; float scale; for (int i = 0; i < len(li); i++) { for (int j = 0; j < nsamples; j++) { nextsample(sid, s.x, s.y, "mode", "nextpixel"); sample_light(li[i], P, s, Time, pos, clr, scale); values[i] += luminance(clr); } values[i] /= nsamples; } // Create a CDF of the power distribution float cdf[] = create_cdf(values); // Randomly select a light based on power distribution nextsample(sid, s.x, s.y, "mode", "nextpixel"); int index = 0; sample_cdf(cdf, s.x, index); // Do something with the selected light // li[index] ...
| See also | |
| bsdf |
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| pbr |
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| sampling |