What common AR computer vision algorithms should you use for AR?

1 Feature detection

Feature detection is the process of finding and describing distinctive points or regions in an image, such as edges, corners, or blobs. These features can be used to match different images of the same scene or object, and to estimate the relative pose and orientation of the camera. Feature detection is essential for AR applications that need to track the position and movement of the device, and to align the virtual content with the real environment. Some of the popular feature detection algorithms are SIFT, SURF, ORB, and FAST.

3 Object recognition

Object recognition is the process of identifying and classifying different objects in an image, based on their appearance, shape, or context. Object recognition can be used to detect and track specific objects of interest, such as faces, logos, or landmarks, and to provide relevant information or actions for them. Object recognition is helpful for AR applications that need to enhance the user experience, and to provide personalized or contextualized content. Some of the widely used object recognition algorithms are Haar Cascade, HOG, and Convolutional Neural Networks (CNNs).

4 Scene reconstruction

Scene reconstruction is the process of creating a 3D model of the real environment, based on multiple images or depth sensors. Scene reconstruction can be used to map and measure the spatial layout and geometry of the scene, and to create a virtual representation of the real world. Scene reconstruction is beneficial for AR applications that need to create immersive and realistic environments, and to enable occlusion-aware rendering and interaction. Some of the common scene reconstruction algorithms are Structure from Motion (SfM), Simultaneous Localization and Mapping (SLAM), and Multi-View Stereo (MVS).

5 Image synthesis

Image synthesis is the process of generating new images or modifying existing images, based on some input or output criteria. Image synthesis can be used to create or edit the virtual content, and to blend it with the real image. Image synthesis is important for AR applications that need to produce high-quality and creative content, and to achieve seamless and natural integration. Some of the popular image synthesis algorithms are Style Transfer, Generative Adversarial Networks (GANs), and Neural Style Transfer.

6 Image registration

Image registration is the process of aligning two or more images of the same scene or object, taken from different viewpoints, times, or sensors. Image registration can be used to combine or fuse the images, and to correct the distortions or discrepancies between them. Image registration is useful for AR applications that need to use multiple sources of information, and to improve the accuracy and robustness of the vision system. Some of the common image registration algorithms are Homography, Affine, and Rigid transformations.

7 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?