Implementing variance shadow mapping

In this recipe, we will cover a technique which gives a much better result, has better performance, and at the same time is easier to calculate. The technique is called variance shadow mapping. In conventional PCF-filtered shadow mapping, we compare the depth value of the current fragment to the mean depth value in the shadow map, and based on the outcome, we shadow the fragment.

In case of variance shadow mapping, the mean depth value (also called first moment) and the mean squared depth value (also called second moment) are calculated and stored. Then, rather than directly using the mean depth, the variance is used. The variance calculation requires both the mean depth as well as the mean of the squared depth. Using the variance, the probability of whether the given sample is shadowed is estimated. This probability is then compared to the maximum probability to determine if the current sample is shadowed.

Getting started

For this recipe, we will build...