Semantic Understanding for Augmented Reality and Its Applications

Abstract

Although augmented reality (AR) devices and developer toolkits are becoming increasingly ubiquitous, current AR devices lack a semantic understanding of the user’s environment. Semantic understanding in an AR context is critical to improving the AR experience because it aids in narrowing the gap between the physical and virtual worlds, making AR more seamless as virtual content interacts naturally with the physical environment. A granular understanding of the user’s environment has the potential to be applied to a wide variety of problems, such as visual output security, improved mesh generation, and semantic map building of the world. This project investigates semantic understanding for AR by building and deploying a system which uses a semantic segmentation model and Magic Leap One to bring semantic understanding to a physical AR device, and explores applications of semantic understanding such as visual output security using reinforcement learning trained policies and the use of semantic context to improve mesh quality.