A Multifactor Comparative Assessment of Augmented Reality Frameworks in Diverse Computing Settings

Abstract

Research and development on different augmented reality (AR) frameworks have come a long way when it comes to image tracking, object tracking, plane tracking and light estimation. However, there might be trade-offs and varying results obtained from different AR frameworks, depending on the use cases, and this is critical for consideration during immersive application development. Besides the current literature effort, this research proposes a multifactor comparative analysis of two core AR frameworks, which aims to analyze and evaluate ARKit and ARCore in diverse computing settings. This research developed a structural application which evaluated three major test parameters across ten devices spanning ARKit and ARCore. The first parameter relates to evaluating AR measurements using four different distance criteria. The second parameter evaluated resource utilization, relating to the central processing unit (CPU) and random access memory (RAM), while the last parameter evaluated plane detection based on light estimation. Findings conclude that ARKit is the preferable AR framework for AR measurement accuracy and reliability within the tested distance criteria. ARCore is the most optimized AR framework in terms of RAM utilization. Regarding plane detection based on light estimation, ARCore is the preferable choice under low lighting conditions, however, ARKit is the most suitable AR framework under adequate ambient lighting conditions. The findings of this research could guide future prototyping and immersive mobile application development within the context of the parameters used.