How computer vision can build more accurate augmented reality environments?

Augmented reality (AR) and associated technologies have become prevalent across a wide range of business applications. AR typically enables users to see the real-world environment right in front of them with a digital augmentation overlaid on it. Though the technology is only going to get bigger, issues like designing an effective augmentation scheme for information, presentation and control and providing precise and robust sensing to determine the surrounding environment must be considered. This is where computer vision has a role to play in providing the appropriate augmentation inducement and addressing the sensing problem in the right position and time.

AR, VR and mixed reality technologies have diverse capabilities. These all are used to create an entirely virtual environment, allowing users to experience a different world surrounding them. To accurately discern and identify the surroundings so that AR can add the virtual world to them requires computer vision. Significantly, computer vision deals with how computers can gain a high-level of understanding from digital images or videos.

What Does Computer Vision Mean to AR?

With advances in immersive technologies, AR can be used for navigation purposes. As it uses GPS, it helps find where a person or object is and their directions. This AR feature is not only intuitive, but also safer than traditional navigation apps. But in congested areas, AR-based navigation systems may not be feasible or can display images at incorrect places and create imprecise AR environments. Leveraging computer vision techniques here can be effective in detecting, identifying and locating real-world elements.

The real value of computer vision is only possible with AI, machine learning and vast sets of data. Its potential to decrypt images and videos has paved the way for various applications including optical character recognition, machine inspection, and 3D model building, among others. Using these applications, AR devices can build their artificial environments and seamlessly merge them with real environments.

AR generally aims at enhancing a person’s perception of the surrounding world, and the integration of computer vision augments this environment. Computer vision for augmented reality devices enables computers to observe, process, evaluate and comprehend digital videos and images. 

Computer vision supports augmented reality with robust vision capabilities like simultaneous localization and mapping (SLAM), which can alternatively provide a geometric position for AR systems. It has the potential to create 3D maps of an environment, along with tracking the location and position of the camera in that environment. This technology estimates the position of the image sensor while at the same time modeling the environment to build a map.

Computer vision simply recognizes what and where an object is, by looking at the object and its appearance, location and the settings. For instance, Snapchat filters that change a person’s face into asymmetrical features. When persons look at photos, they see their face, while computer vision sees data. It then maps facial features from photos or videos, reads the geometry of the face, captures facial landmarks and takes into account the human face.

AR has attracted a lot of eyes from businesses in diverse industries, giving them opportunities to see and learn about things more accurately and precisely in a virtual environment.