Future of AR and VR with Advanced AI Data
By Lucy Hur
Augmented reality (AR) augments an individual’s surroundings by adding a new digital environment to reality. Virtual reality (VR), on the other hand, uses computer-generated imagery (CG) to replace a real-life environment with a simulated one. To maximize the VR experience, users wear headsets to ensure their point of view is fully immersed in the simulated environment.
You might not realize it but AI is already shaping the future of AR and VR. Have you heard of Snapchat? What about Pokémon Go? These are all innovative examples of AR where a digital filter is added on top of reality. Even social media filters are AR features used by millions on a daily basis — the filters are superimposed over real-world camera images.
Companies are already applying AI to create a better AR experience for their users. Google Glass allows users to interact with the external world through voice commands and touch input overlays in their real-life environment. AI brings a more immersive experience for AR/VR users using deep learning techniques such as convolutional neural networks (CNN). The user’s AR experiences can be enhanced by advancing accurate object recognition tasks including localization and classification. CNN-object detection and recognition can also be applied to semantic segmentation. Unlike simple object recognition, semantic segmentation would enable labeling of all pixels of the environment, including the sky, road, cars, and pedestrians. This has many potential uses, from self-driving car navigation to VR flight simulations. Accuracy in detecting surroundings and objects is improved, and interactions between real and virtual objects in multidimensions become more immersive.
Deep learning tools like CNN will constantly evolve AR systems, improving our experiences in gaming, autonomous vehicles, and more. Today, R&D researchers are hard at work building these AR/VR systems with strong performance and area detection requirements while satisfying increasingly demanding user requests.
