Book Image

3D Graphics Rendering Cookbook

By : Sergey Kosarevsky, Viktor Latypov
4 (2)
Book Image

3D Graphics Rendering Cookbook

4 (2)
By: Sergey Kosarevsky, Viktor Latypov

Overview of this book

OpenGL is a popular cross-language, cross-platform application programming interface (API) used for rendering 2D and 3D graphics, while Vulkan is a low-overhead, cross-platform 3D graphics API that targets high-performance applications. 3D Graphics Rendering Cookbook helps you learn about modern graphics rendering algorithms and techniques using C++ programming along with OpenGL and Vulkan APIs. The book begins by setting up a development environment and takes you through the steps involved in building a 3D rendering engine with the help of basic, yet self-contained, recipes. Each recipe will enable you to incrementally add features to your codebase and show you how to integrate different 3D rendering techniques and algorithms into one large project. You'll also get to grips with core techniques such as physically based rendering, image-based rendering, and CPU/GPU geometry culling, to name a few. As you advance, you'll explore common techniques and solutions that will help you to work with large datasets for 2D and 3D rendering. Finally, you'll discover how to apply optimization techniques to build performant and feature-rich graphics applications. By the end of this 3D rendering book, you'll have gained an improved understanding of best practices used in modern graphics APIs and be able to create fast and versatile 3D rendering frameworks.
Table of Contents (12 chapters)

Precomputing irradiance maps and diffuse convolution

The second part of the split sum approximation necessary to calculate the glTF2 physically based shading model comes from the irradiance cube map, which is precalculated by convolving the input environment cube map with the GGX distribution of our shading model.

Getting ready

Check out the source code for this recipe in Chapter6/Util01_FilterEnvmap. If you want to dive deep into the math theory behind these computations, make sure you read Brian Karis's paper at https://cdn2.unrealengine.com/Resources/files/2013SiggraphPresentationsNotes-26915738.pdf.

How to do it...

This code is written for simplicity rather than for speed or precision, so it does not use importance sampling and convolves the input cube map using simple Monte Carlo integration and the Hammersley sequence to generate uniformly distributed 2D points on an equirectangular projection of our input cube map.

The source code can be found in the Chapter6...