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

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...