Jaguar

Abstract

In this paper, we present the design, implementation and evaluation of Jaguar, a mobile Augmented Reality (AR) system that features accurate, low-latency, and large-scale object recognition and flexible, robust, and context-aware tracking. Jaguar pushes the limit of mobile AR's end-to-end latency by leveraging hardware acceleration with GPUs on edge cloud. Another distinctive aspect of Jaguar is that it seamlessly integrates marker-less object tracking offered by the recently released AR development tools (e.g., ARCore and ARKit) into its design. Indeed, some approaches used in Jaguar have been studied before in a standalone manner, e.g., it is known that cloud offloading can significantly decrease the computational latency of AR. However, the question of whether the combination of marker-less tracking, cloud offloading and GPU acceleration would satisfy the desired end-to-end latency of mobile AR (i.e., the interval of camera frames) has not been eloquently addressed yet. We demonstrate via a prototype implementation of our proposed holistic solution that Jaguar reduces the end-to-end latency to ~33 ms. It also achieves accurate six degrees of freedom tracking and 97% recognition accuracy for a dataset with 10,000 images.