  Metric depth estimation plays an important role in mobile augmented reality
(AR). With accurate metric depth, we can achieve more realistic user
interactions such as object placement and occlusion detection. While
specialized hardware like LiDAR demonstrates its promise, its restricted
availability, i.e., only on selected high-end mobile devices, and performance
limitations such as range and sensitivity to the environment, make it less
ideal. Monocular depth estimation, on the other hand, relies solely on mobile
cameras, which are ubiquitous, making it a promising alternative for mobile AR.
  In this paper, we investigate the challenges and opportunities of achieving
accurate metric depth estimation in mobile AR. We tested four different
state-of-the-art monocular depth estimation models on a newly introduced
dataset (ARKitScenes) and identified three types of challenges: hard-ware,
data, and model related challenges. Furthermore, our research provides
promising future directions to explore and solve those challenges. These
directions include (i) using more hardware-related information from the mobile
device's camera and other available sensors, (ii) capturing high-quality data
to reflect real-world AR scenarios, and (iii) designing a model architecture to
utilize the new information.
