  Predicting depth from a single image is an attractive research topic since it
provides one more dimension of information to enable machines to better
perceive the world. Recently, deep learning has emerged as an effective
approach to monocular depth estimation. As obtaining labeled data is costly,
there is a recent trend to move from supervised learning to unsupervised
learning to obtain monocular depth. However, most unsupervised learning methods
capable of achieving high depth prediction accuracy will require a deep network
architecture which will be too heavy and complex to run on embedded devices
with limited storage and memory spaces. To address this issue, we propose a new
powerful network with a recurrent module to achieve the capability of a deep
network while at the same time maintaining an extremely lightweight size for
real-time high performance unsupervised monocular depth prediction from video
sequences. Besides, a novel efficient upsample block is proposed to fuse the
features from the associated encoder layer and recover the spatial size of
features with the small number of model parameters. We validate the
effectiveness of our approach via extensive experiments on the KITTI dataset.
Our new model can run at a speed of about 110 frames per second (fps) on a
single GPU, 37 fps on a single CPU, and 2 fps on a Raspberry Pi 3. Moreover, it
achieves higher depth accuracy with nearly 33 times fewer model parameters than
state-of-the-art models. To the best of our knowledge, this work is the first
extremely lightweight neural network trained on monocular video sequences for
real-time unsupervised monocular depth estimation, which opens up the
possibility of implementing deep learning-based real-time unsupervised
monocular depth prediction on low-cost embedded devices.
