Efficient Single-Image Depth Estimation on Mobile Devices, Mobile AI & AIM 2022 Challenge: Report
This work addresses the need for fast and accurate depth estimation on low-power mobile and IoT devices for applications like image segmentation and object tracking, but it is incremental as it builds on existing challenge frameworks and datasets.
The paper tackled the problem of efficient single-image depth estimation for mobile devices by developing deep learning models that achieve real-time performance, generating VGA resolution depth maps at up to 27 FPS on Raspberry Pi 4 while maintaining high fidelity.
Various depth estimation models are now widely used on many mobile and IoT devices for image segmentation, bokeh effect rendering, object tracking and many other mobile tasks. Thus, it is very crucial to have efficient and accurate depth estimation models that can run fast on low-power mobile chipsets. In this Mobile AI challenge, the target was to develop deep learning-based single image depth estimation solutions that can show a real-time performance on IoT platforms and smartphones. For this, the participants used a large-scale RGB-to-depth dataset that was collected with the ZED stereo camera capable to generated depth maps for objects located at up to 50 meters. The runtime of all models was evaluated on the Raspberry Pi 4 platform, where the developed solutions were able to generate VGA resolution depth maps at up to 27 FPS while achieving high fidelity results. All models developed in the challenge are also compatible with any Android or Linux-based mobile devices, their detailed description is provided in this paper.