When a user scans a QR code on a product and instantly sees a 3D animation appear on top of it with no app, just a browser, it looks like magic. But, it isn’t. It’s the result of several computer vision, graphics, and web technologies working together in real time on a device that fits in your pocket.
Understanding how WebAR actually works won’t just satisfy your technical curiosity. It’ll help you make better decisions about what’s achievable in a campaign, why certain tracking types behave differently, and how to brief your development team (or evaluate a platform) with more confidence.
This guide explains the full stack: camera access, SLAM tracking, WebGL rendering, and the browser APIs that tie it all together.
The starting point: camera access in the browser
Everything in WebAR starts with the camera feed. Before any AR tracking can happen, your browser needs permission to access the device camera.
This is handled through WebRTC (Web Real-Time Communication), a browser API that allows web applications to access media streams from device hardware, including the camera and microphone. When an AR experience loads in your browser, the first thing it does is request camera access. If you’ve ever seen a permission prompt asking “Allow this site to use your camera?”, that’s WebRTC making the request.
Once camera access is granted, the raw video feed is piped into the AR engine. From here, the heavy lifting begins.
Computer Vision: reading the real world
With the camera feed running, the AR engine needs to understand what it’s looking at. This is the computer vision layer, which is the part that turns raw pixels into meaningful spatial information.
Different tracking types use different computer vision approaches. These include:
Marker (Image) Tracking
The simplest form of WebAR tracking. The engine looks for a specific 2D image, which could be a product label, a poster or a QR code, and calculates its position and orientation in 3D space. When the marker is detected, AR content is anchored to it. As the user moves the phone, the engine continuously recalculates the marker’s position to keep the content aligned.
This works by extracting feature points from the camera frame, from distinct corners, edges, and texture patterns that can be reliably identified across different lighting conditions and viewing angles. The detected feature points in the live frame are compared against a reference map of the target image. When enough points match, the engine has a confident lock on the marker’s 3D pose.
Face tracking
Face tracking uses a combination of machine learning models and geometric estimation to identify and track facial landmarks including the positions of eyes, nose, mouth, and jawline, in real time. Once the face mesh is established, AR content (filters, glasses, makeup, animated effects) can be anchored to specific facial points and updated every frame as the face moves.
Surface (world) tracking
Surface tracking is more complex. Rather than looking for a predefined target, the engine analyses the environment to detect flat planes from floors and tables to walls, and creates a basic 3D map of the space. AR objects can then be placed on these surfaces and appear anchored to the real world even as the user walks around them.
Surface tracking is where SLAM comes in.
SLAM: building a map of the world in real time
SLAM stands for Simultaneous Localization and Mapping. It’s the technology that enables AR experiences to track the user’s position in 3D space and maintain the illusion that virtual objects are anchored in the real world, even when the user moves, turns, or steps back.
Here’s what it does, step by step:
- Feature extraction: As each camera frame arrives, the SLAM engine identifies distinctive visual features including corners, edges and gradients that are likely to be stable and trackable across multiple frames. These become the engine’s “landmarks.”
- Triangulation and map building: As the user moves, the same landmarks appear from different angles. By comparing how their apparent positions shift between frames, the engine can triangulate their 3D positions in space. This builds up a sparse 3D point cloud which is a rough map of the environment.
- Pose estimation: With a 3D map established, the engine can calculate where the camera is within that map at any given moment, its position (X, Y, Z coordinates) and orientation (pitch, yaw, roll). This is the “localization” part of SLAM.
- AR content anchoring: Once the engine knows both the map and the camera’s position within it, it can place a virtual object at a specific map coordinate and keep it there. Every new frame, the engine updates the camera pose and adjusts the rendering accordingly.
In a browser-based SLAM implementation, this entire pipeline runs in real time using WebAssembly, a low-level binary format that executes near-native performance in the browser. The compute-intensive parts of SLAM (feature matching, pose estimation, map updating) are implemented in compiled WebAssembly code, which allows them to run fast enough on mobile hardware to stay in sync with the camera frame rate.
WebGL: rendering the AR scene
Tracking tells the engine where things are. Rendering is what makes them visible.
WebGL (Web Graphics Library) is a JavaScript API that gives browser-based applications direct access to the device’s GPU (Graphics Processing Unit). Without WebGL, any 3D rendering in the browser would be done entirely on the CPU, far too slow for real-time AR.
In a WebAR experience, WebGL handles:
- Loading and displaying 3D models by parsing 3D asset files (typically glTF or FBX format), uploading geometry and textures to the GPU, and drawing them to the screen
- Camera background rendering by compositing the live camera feed as the background layer of the scene
- Lighting and shading by applying material properties, light sources, and shadow effects to make 3D objects look like they belong in the real environment
- Frame-rate management by coordinating with the browser’s rendering loop to draw a new scene on every frame (typically targeting 30 or 60 frames per second).
- WebGPU is the next-generation successor to WebGL, offering even closer access to GPU hardware and significantly better performance for complex scenes. Browser support is growing in 2025 – 2026, and WebAR platforms are beginning to adopt it for higher-fidelity rendering.
The full pipeline: from camera feed to AR experience
Putting it all together, here’s what happens in the time between a user tapping an AR link and seeing a 3D object appear on their table:
- Browser requests camera access via WebRTC
- Camera feed begins streaming and raw video frames arrive at the AR engine
- Computer vision layer activates and the engine begins scanning for the relevant tracking target
- SLAM initialises and feature points are extracted, a 3D map begins to form, camera pose is estimated
- Tracking lock established so the engine has enough confidence in the camera’s position to anchor content
- WebGL renderer activates, the 3D scene is constructed, the AR object is placed at the tracked coordinate, and the camera projection matrix is applied
- Composite frame rendered, the live camera feed and the 3D scene are composited together and drawn to the screen
- Loop continues at 30–60fps, on every new frame, pose is updated, the 3D scene is re-rendered, and the composite is drawn again
This entire process happens locally on the user’s device. Nothing is sent to a server for processing. The privacy implications are worth noting: the camera feed is never transmitted anywhere; all the tracking and rendering compute happens client-side.
Why this architecture enables no-app AR
The reason WebAR doesn’t require an app isn’t just a convenience decision, it’s a consequence of the architecture. All of the core technologies involved (WebRTC, WebAssembly, WebGL) are standard browser APIs, implemented natively in Chrome, Safari, and Firefox.
This means an AR experience is just a web page. It loads like a web page, runs like a web page, and can be linked to like a web page. The AR engine is JavaScript and WebAssembly code that loads in the browser alongside everything else. There’s nothing to install because there’s nothing fundamentally different about it from any other web application, except that it uses the camera and GPU rather than just displaying text and images.
This is also why WebAR can be delivered via QR code, link, email, social media, or NFC with zero friction. The distribution model is just a URL.
What this means for campaign decisions
Understanding the technical architecture helps explain some of the practical constraints you’ll encounter in WebAR production:
- Asset optimisation matters a lot. WebGL renders directly to the GPU, but mobile GPUs have limits. 3D models for WebAR should be kept under ~50k polygons for reliable performance, with compressed textures. High-poly models that work in a desktop 3D tool will often run poorly in a mobile WebAR experience without optimisation.
- Tracking quality depends on the environment. SLAM relies on distinctive visual features. Environments with flat, featureless surfaces (plain white walls, uniform floors) give the engine less to work with, which can result in slower initialisation or drift. Good lighting and visual texture in the environment improve tracking reliability.
- Browser and device fragmentation is real. While WebGL and WebRTC are widely supported, there are differences in how browsers expose the camera API on iOS vs Android. Production-grade WebAR SDKs include compatibility layers and fallbacks to smooth this out, but it’s worth testing across target devices before launch.
- Loading time is a user experience consideration. The AR engine, 3D assets, and textures all need to load before the experience starts. Optimising asset file sizes and using progressive loading reduces the time users spend on a loading screen.
Building on this foundation
The technologies described here including WebRTC, SLAM, WebAssembly and WebGL, form the foundation of all modern WebAR. Platforms like Blippar’s WebAR SDK abstract most of this complexity, exposing straightforward APIs for tracking types, scene management, and analytics so that developers can focus on building experiences rather than implementing computer vision from scratch.
If you’re exploring what WebAR can do technically, the best way to understand it is to build something. The Blippar WebAR SDK includes full documentation, framework integrations (A-Frame, Babylon.js, PlayCanvas, Unity), and a free developer tier with no payment details required.
Start building WebAR today
If you’re ready to explore what WebAR can do for your next campaign, or want to understand what your first experience could look like, talk to the Blippar team.
Click here to book a discovery call with Blippar Studios to see how we can bring your campaign to life.
Sources and further reading
How Much Does WebAR Cost in 2026?