Book Image

Learn ARCore - Fundamentals of Google ARCore

Book Image

Learn ARCore - Fundamentals of Google ARCore

Overview of this book

Are you a mobile developer or web developer who wants to create immersive and cool Augmented Reality apps with the latest Google ARCore platform? If so, this book will help you jump right into developing with ARCore and will help you create a step by step AR app easily. This book will teach you how to implement the core features of ARCore starting from the fundamentals of 3D rendering to more advanced concepts such as lighting, shaders, Machine Learning, and others. We’ll begin with the basics of building a project on three platforms: web, Android, and Unity. Next, we’ll go through the ARCore concepts of motion tracking, environmental understanding, and light estimation. For each core concept, you’ll work on a practical project to use and extend the ARCore feature, from learning the basics of 3D rendering and lighting to exploring more advanced concepts. You’ll write custom shaders to light virtual objects in AR, then build a neural network to recognize the environment and explore even grander applications by using ARCore in mixed reality. At the end of the book, you’ll see how to implement motion tracking and environment learning, create animations and sounds, generate virtual characters, and simulate them on your screen.
Table of Contents (17 chapters)
Title Page
Packt Upsell

AR and ARCore

Augmented reality applications are unique in that they annotate or augment the reality of the user. This is typically done visually by having the AR app overlay a view of the real world with computer graphics. ARCore is designed primarily for providing this type of visual annotation for the user. An example of a demo ARCore application is shown here:

Google ARCore demo application; the dog is real

The screenshot is even more impressive when you realize that it was rendered real time on a mobile device. It isn't the result of painstaking hours of using Photoshop or other media effects libraries. What you see in that image is the entire superposition of a virtual object, the lion, into the user's reality. More impressive still is the quality of immersion. Note the details, such as the lighting and shadows on the lion, the shadows on the ground, and the way the object maintains position in reality even though it isn't really there. Without those visual enhancements, all you would see is a floating lion superimposed on the screen. It is those visual details that provide the immersion. Google developed ARCore as a way to help developers incorporate those visual enhancements in building AR applications.


Google developed ARCore for Android as a way to compete against Apple's ARKit for iOS. The fact that two of the biggest tech giants today are vying for position in AR indicates the push to build new and innovative immersive applications.

ARCore has its origins in Tango, which is/was a more advanced AR toolkit that used special sensors built into the device. In order to make AR more accessible and mainstream, Google developed ARCore as an AR toolkit designed for Android devices not equipped with any special sensors. Where Tango depended on special sensors, ARCore uses software to try and accomplish the same core enhancements. For ARCore, Google has identified three core areas to address with this toolkit, and they are as follows:

  • Motion tracking
  • Environmental understanding
  • Light estimation

In the next three sections, we will go through each of those core areas in more detail and understand how they enhance the user experience.

Motion tracking

Tracking a user's motion and ultimately their position in 2D and 3D space is fundamental to any AR application. ARCore allows us to track position changes by identifying and tracking visual feature points from the device's camera image. An example of how this works is shown in this figure:

Feature point tracking in ARCore

In the figure, we can see how the user's position is tracked in relation to the feature points identified on the real couch. Previously, in order to successfully track motion (position), we needed to pre-register or pre-train our feature points. If you have ever used the Vuforia AR tools, you will be very familiar with having to train images or target markers. Now, ARCore does all this automatically for us, in real time, without any training. However, this tracking technology is very new and has several limitations. In the later part of the book, and specifically in Chapter 5, Real-World Motion Tracking, we will add a feature to our AR assistant that allows us to track multiple objects' positions from multiple devices in real time using GPS. Then, in Chapter 10Mixing in Mixed Reality, we will extend our tracking to include augmented maps.

Environmental understanding

The better an AR application understands the user's reality or the environment around them, the more successful the immersion. We already saw how ARCore uses feature identification in order to track a user's motion. Yet, tracking motion is only the first part. What we need is a way to identify physical objects or surfaces in the user's reality. ARCore does this using a technique called meshing.

We will cover more details about meshing in later chapters, but, for now, take a look at the following figure from Google that shows this meshing operation in action:

Google image showing meshing in action

What we see happening in the preceding image is an AR application that has identified a real-world surface through meshing. The plane is identified by the white dots. In the background, we can see how the user has already placed various virtual objects on the surface. Environmental understanding and meshing are essential for creating the illusion of blended realities. Where motion tracking uses identified features to track the user's position, environmental understanding uses meshing to track the virtual objects in the user's reality. In Chapter 8, Recognizing the Environment, we will look at how to train our own machine learning object identifier, which will allow us to extend our meshing to include automatically recognizable objects or areas of an environment.

Light estimation

Magicians work to be masters of trickery and visual illusion. They understand that perspective and good lighting are everything in a great illusion, and, with developing great AR apps, this is no exception. Take a second and flip back to the scene with the virtual lion. Note the lighting and detail in the shadows on the lion and ground. Did you note that the lion is casting a shadow on the ground, even though it's not really there? That extra level of lighting detail is only made possible by combining the tracking of the user's position with the environmental understanding of the virtual object's position and a way to read light levels. Fortunately, ARCore provides us with a way to read or estimate the light in a scene. We can then use this lighting information in order to light and shadow virtual AR objects. Here's an image of an ARCore demo app showing subdued lighting on an AR object:

Google image of demo ARCore app showing off subdued lighting

The effects of lighting, or lack thereof, will become more obvious as we start developing our startup applications. Later, in Chapter 9Blending Light for Architectural Design, we will go into far more detail about 3D lighting and even build some simple shader effects.


In this chapter, we didn't go into any extensive details; we will get to that later, but you should now have a good grasp of the core elements ARCore was developed to address. In the next section, we will take a closer look at how best to use the material in this book.