Book Image

OpenGL Development Cookbook

By : Muhammad Mobeen Movania
Book Image

OpenGL Development Cookbook

By: Muhammad Mobeen Movania

Overview of this book

OpenGL is the leading cross-language, multi-platform API used by masses of modern games and applications in a vast array of different sectors. Developing graphics with OpenGL lets you harness the increasing power of GPUs and really take your visuals to the next level. OpenGL Development Cookbook is your guide to graphical programming techniques to implement 3D mesh formats and skeletal animation to learn and understand OpenGL. OpenGL Development Cookbook introduces you to the modern OpenGL. Beginning with vertex-based deformations, common mesh formats, and skeletal animation with GPU skinning, and going on to demonstrate different shader stages in the graphics pipeline. OpenGL Development Cookbook focuses on providing you with practical examples on complex topics, such as variance shadow mapping, GPU-based paths, and ray tracing. By the end you will be familiar with the latest advanced GPU-based volume rendering techniques.
Table of Contents (15 chapters)
OpenGL Development Cookbook
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Index

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...