METER: a mobile vision transformer architecture for monocular depth
  estimation
L. Papa P. Russo I. Amerini
Depth estimation is a fundamental knowledge for autonomous systems that need
to assess their own state and perceive the surrounding environment. Deep
learning algorithms for depth estimation have gained significant interest in
recent years, owing to the potential benefits of this methodology in overcoming
the limitations of active depth sensing systems. Moreover, due to the low cost
and size of monocular cameras, researchers have focused their attention on
monocular depth estimation (MDE), which consists in estimating a dense depth
map from a single RGB video frame. State of the art MDE models typically rely
on vision transformers (ViT) architectures that are highly deep and complex,
making them unsuitable for fast inference on devices with hardware constraints.
Purposely, in this paper, we address the problem of exploiting ViT in MDE on
embedded devices. Those systems are usually characterized by limited memory
capabilities and low-power CPU/GPU. We propose METER, a novel lightweight
vision transformer architecture capable of achieving state of the art
estimations and low latency inference performances on the considered embedded
hardwares: NVIDIA Jetson TX1 and NVIDIA Jetson Nano. We provide a solution
consisting of three alternative configurations of METER, a novel loss function
to balance pixel estimation and reconstruction of image details, and a new data
augmentation strategy to improve the overall final predictions. The proposed
method outperforms previous lightweight works over the two benchmark datasets:
the indoor NYU Depth v2 and the outdoor KITTI.