Book Image

Mastering Graphics Programming with Vulkan

By : Marco Castorina, Gabriel Sassone
5 (1)
Book Image

Mastering Graphics Programming with Vulkan

5 (1)
By: Marco Castorina, Gabriel Sassone

Overview of this book

Vulkan is now an established and flexible multi-platform graphics API. It has been adopted in many industries, including game development, medical imaging, movie productions, and media playback. Learning Vulkan is a foundational step to understanding how a modern graphics API works, both on desktop and mobile. In Mastering Graphics Programming with Vulkan, you’ll begin by developing the foundations of a rendering framework. You’ll learn how to leverage advanced Vulkan features to write a modern rendering engine. The chapters will cover how to automate resource binding and dependencies. You’ll then take advantage of GPU-driven rendering to scale the size of your scenes and finally, you’ll get familiar with ray tracing techniques that will improve the visual quality of your rendered image. By the end of this book, you’ll have a thorough understanding of the inner workings of a modern rendering engine and the graphics techniques employed to achieve state-of-the-art results. The framework developed in this book will be the starting point for all your future experiments.
Table of Contents (21 chapters)
1
Part 1: Foundations of a Modern Rendering Engine
7
Part 2: GPU-Driven Rendering
13
Part 3: Advanced Rendering Techniques

Further reading

We have only provided a brief introduction to screen-space reflections. The following articles go into more detail about their implementation, their limitations, and how to improve the final results:

We have only used one of the many hashing techniques presented in the paper Hash Functions for GPU Rendering: https://jcgt.org/published/0009/03/02/.

This link contains more details about the sampling technique we used to determine the reflection vector by sampling the BRDF – Sampling the GGX Distribution of Visible Normals: https://jcgt.org/published/0007/04/01/.

For more details about the SVGF algorithm we presented, we recommend reading the original paper and supporting material...