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

Implementing a denoiser

To make the output of our reflection pass usable for lighting computations, we need to pass it through a denoiser. We have implemented an algorithm called SVGF, which has been developed to reconstruct color data for path tracing.

SVGF consists of three main passes:

  1. First, we compute the integrated color and moments for luminance. This is the temporal step of the algorithm. We combine the data from the previous frame with the result of the current frame.
  2. Next, we compute an estimate for variance. This is done using the first and second moment values we computed in the first step.
  3. Finally, we perform five passes of a wavelet filter. This is the spatial step of the algorithm. At each iteration, we apply a 5x5 filter to reduce the remaining noise as much as possible.

Now that you have an idea of the main algorithm, we can proceed with the code details. We start by computing the moments for the current frame:

float u_1 = luminance( reflections_color...