Book Image

Mastering Graphics Programming with Vulkan

By : Marco Castorina, Gabriel Sassone
5 (2)
Book Image

Mastering Graphics Programming with Vulkan

5 (2)
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 but learning it can be a daunting challenge due to its low-level, complex nature. Mastering Graphics Programming with Vulkan is designed to help you overcome this difficulty, providing a practical approach to learning one of the most advanced graphics APIs. In Mastering Graphics Programming with Vulkan, you’ll focus on building a high-performance rendering engine from the ground up. You’ll explore Vulkan’s advanced features, such as pipeline layouts, resource barriers, and GPU-driven rendering, to automate tedious tasks and create efficient workflows. Additionally, you'll delve into cutting-edge techniques like mesh shaders and real-time ray tracing, elevating your graphics programming to the next level. By the end of this book, you’ll have a thorough understanding of modern rendering engines to confidently handle large-scale projects. Whether you're developing games, simulations, or visual effects, this guide will equip you with the skills and knowledge to harness Vulkan’s full potential.
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...