Augmented Reality (AR) Course

Lesson 1 • Setting Up Your AR Learning Environment

Guides learners through installing required SDKs, emulators, and development tools. Ensures every learner has a functional workspace before hands-on exercises begin.

Lesson 2 • Defining AR and the XR Spectrum

Clarifies the precise definition of AR and differentiates it from VR, MR, and XR. Provides the vocabulary needed for all subsequent technical and design discussions.

Lesson 3 • AR Use Cases Across Industries

Surveys validated AR applications in manufacturing, healthcare, retail, education, and entertainment. Motivates learners by connecting theory to real-world professional impact.

Lesson 4 • Core AR System Components

Identifies the hardware and software layers that every AR system requires. Builds a mental model learners will reference when designing and troubleshooting AR experiences.

Lesson 1 • Markerless and SLAM Tracking

Introduces simultaneous localisation and mapping (SLAM) for environment-aware AR without predefined targets. Learners compare SLAM performance against marker-based approaches.

Lesson 2 • Principles of Spatial Tracking

Explains how AR systems estimate device pose in 3D space using sensor fusion. Establishes the mathematical intuition behind translation, rotation, and scale alignment.

Lesson 3 • Evaluating and Improving Tracking Quality

Provides metrics and diagnostic tools for measuring tracking stability and registration error. Learners apply optimisation techniques to reduce jitter and improve user experience.

Lesson 4 • Marker-Based Tracking

Covers fiducial markers, QR-style targets, and image targets as anchors for AR content. Learners implement a working marker-tracked scene by the end of this section.

Lesson 1 • Asset Optimisation for Real-Time AR

Applies draw call reduction, batching, and occlusion culling to meet AR frame-rate targets. Learners benchmark and optimise a scene to achieve stable 60 fps on a mobile device.

Lesson 2 • 3D Modelling Essentials for AR

Introduces polygon modelling, mesh topology, and scale conventions specific to AR contexts. Establishes asset creation habits that prevent performance issues downstream.

Lesson 3 • Animation and Rigging Basics

Teaches skeletal rigging, keyframe animation, and blend shapes for interactive AR characters. Connects animation concepts to trigger-based playback in AR scenes.

Lesson 4 • Asset Export and Format Standards

Reviews glTF, FBX, OBJ, and USDZ formats and their compatibility with major AR platforms. Learners export assets in multiple formats and validate them in a target AR engine.

Lesson 1 • AR Engine Landscape Overview

Compares major AR development platforms by capability, licensing, and target platform support. Helps learners choose the right engine for specific project requirements.

Lesson 2 • Building and Deploying to Devices

Walks through build configuration, signing, and deployment to iOS and Android AR-capable devices. Learners resolve common build errors and confirm app functionality on hardware.

Lesson 3 • Scene Setup and AR Session Management

Covers AR session initialisation, camera rig configuration, and lifecycle management in a chosen engine. Learners create a stable AR session that persists across app states.

Lesson 4 • Placing and Anchoring Virtual Objects

Implements raycasting, hit testing, and anchor creation to place objects on detected surfaces. Learners build a scene where users tap to place and reposition 3D content.

Lesson 1 • Object Detection and Recognition

Implements real-time object detection models to trigger AR overlays on recognised items. Learners deploy a pre-trained detection model within an AR scene.

Lesson 2 • Depth Sensing and 3D Reconstruction

Explores LiDAR, structured light, and stereo depth sensors for mesh reconstruction in AR. Learners use depth data to enable occlusion and surface-aware content placement.

Lesson 3 • Image Processing Fundamentals

Reviews colour spaces, filtering, edge detection, and feature extraction as prerequisites for AR vision tasks. Grounds learners in the signal-processing layer beneath higher-level AR APIs.

Lesson 4 • Face and Body Tracking

Covers facial landmark detection, body pose estimation, and their use in AR filters and overlays. Learners build a face-filter effect and a body-pose-driven AR interaction.

Lesson 1 • Accessibility in AR Experiences

Applies contrast, motion sensitivity, and alternative input guidelines to make AR inclusive. Learners audit an existing AR prototype against accessibility criteria.

Lesson 2 • Usability Testing for AR Interfaces

Designs and conducts moderated usability tests specific to AR's physical and spatial context. Learners analyse test results and iterate on UI designs based on findings.

Lesson 3 • Input Methods for AR Interaction

Covers touch, gaze, gesture, voice, and controller inputs available across AR hardware platforms. Learners implement at least two input modalities in a single AR scene.

Lesson 4 • Spatial UI Design Principles

Establishes rules for placing UI elements in 3D space, including depth, scale, and legibility. Differentiates world-locked, body-locked, and head-locked UI placement strategies.

Lesson 1 • Cloud Anchor and Spatial Persistence

Uses cloud anchor services to store and retrieve AR content positions across sessions and devices. Learners create a persistent AR installation that survives app restarts.

Lesson 2 • Collaborative AR Session Design

Designs shared coordinate systems, authority models, and conflict resolution for multi-user AR. Learners build a collaborative AR scene where users manipulate shared objects.

Lesson 3 • Real-Time Networking for AR

Covers WebSocket, UDP, and dedicated multiplayer SDKs for low-latency AR state synchronisation. Learners implement real-time object position sync between two AR clients.

Lesson 4 • Security and Privacy in Connected AR

Addresses data encryption, user consent, and spatial data privacy risks in networked AR systems. Learners apply security best practices to a cloud-connected AR prototype.

Lesson 1 • Quality Assurance for AR Applications

Designs AR-specific test plans covering tracking stability, rendering correctness, and edge cases. Learners execute a full QA cycle and document defects with reproducible steps.

Lesson 2 • AR Project Planning and Scoping

Defines AR project requirements, constraints, and success metrics using structured discovery methods. Connects planning decisions to downstream development and testing efficiency.

Lesson 3 • Performance Profiling and Optimisation

Uses CPU, GPU, and memory profilers to identify and resolve AR performance bottlenecks. Learners reduce a sample app's frame time by at least 30% through targeted fixes.

Lesson 4 • App Store Submission and Compliance

Navigates platform submission requirements, AR permission declarations, and content guidelines. Learners prepare a submission-ready build with all required metadata and assets.