How does AR object detection work?

1 Feature-based detection

One of the most common methods for AR object detection is feature-based detection. This technique involves finding distinctive points or features on an object, such as corners, edges, or blobs, and matching them with a reference image or a database of images. For example, if you want to detect a book cover in AR, you can extract features from the cover image and compare them with the features from the camera feed. If there is a high enough similarity, you can locate the book and overlay digital content on it. Feature-based detection is useful for detecting flat or textured objects, such as logos, paintings, or magazines.

2 Marker-based detection

Another method for AR object detection is marker-based detection. This technique relies on using predefined symbols or patterns, called markers, that are easy to recognize and track by the AR application. For example, you can use QR codes, barcodes, or fiducial markers as anchors for AR content. The AR application scans the camera feed for the markers and calculates their position, orientation, and size. Then, it renders the digital content on top of them. Marker-based detection is useful for creating simple and robust AR experiences, such as product information, interactive games, or educational content.

3 Plane detection

A more advanced method for AR object detection is plane detection. This technique involves finding flat surfaces in the user's environment, such as floors, walls, or tables, and estimating their boundaries and orientation. Plane detection is often based on depth sensors, such as LiDAR or stereo cameras, that can measure the distance between the camera and the objects. Alternatively, plane detection can also use computer vision algorithms, such as RANSAC or homography, that can infer the geometry of the scene from the camera feed. Plane detection is useful for placing virtual objects on realistic surfaces, such as furniture, toys, or characters.

4 Model-based detection

The most complex method for AR object detection is model-based detection. This technique involves using 3D models of the objects that you want to detect and track in AR. For example, you can use a 3D model of a car, a face, or a hand as a reference for AR object detection. The AR application compares the 3D model with the camera feed and tries to find the best match, using techniques such as edge detection, silhouette matching, or pose estimation. Model-based detection is useful for detecting and tracking non-flat or non-textured objects, such as human body parts, animals, or vehicles.

5 Challenges and opportunities

AR object detection is not a trivial task. It faces many challenges, such as varying lighting conditions, occlusions, camera noise, or dynamic scenes. To overcome these challenges, AR developers need to use robust and efficient algorithms, optimize their performance, and test their accuracy. However, AR object detection also offers many opportunities for creating engaging and innovative AR applications, such as face filters, virtual try-ons, augmented reality games, or interactive learning. AR object detection is a key component of AR technology and a fascinating field of research and development.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?