  Depth estimation is an important computer vision problem with many practical
applications to mobile devices. While many solutions have been proposed for
this task, they are usually very computationally expensive and thus are not
applicable for on-device inference. To address this problem, we introduce the
first Mobile AI challenge, where the target is to develop an end-to-end deep
learning-based depth estimation solutions that can demonstrate a nearly
real-time performance on smartphones and IoT platforms. For this, the
participants were provided with a new large-scale dataset containing RGB-depth
image pairs obtained with a dedicated stereo ZED camera producing
high-resolution depth maps for objects located at up to 50 meters. The runtime
of all models was evaluated on the popular Raspberry Pi 4 platform with a
mobile ARM-based Broadcom chipset. The proposed solutions can generate VGA
resolution depth maps at up to 10 FPS on the Raspberry Pi 4 while achieving
high fidelity results, and are compatible with any Android or Linux-based
mobile devices. A detailed description of all models developed in the challenge
is provided in this paper.
