  Estimating a depth map from a single RGB image has been investigated widely
for localization, mapping, and 3-dimensional object detection. Recent studies
on a single-view depth estimation are mostly based on deep Convolutional neural
Networks (ConvNets) which require a large amount of training data paired with
densely annotated labels. Depth annotation tasks are both expensive and
inefficient, so it is inevitable to leverage RGB images which can be collected
very easily to boost the performance of ConvNets without depth labels. However,
most self-supervised learning algorithms are focused on capturing the semantic
information of images to improve the performance in classification or object
detection, not in depth estimation. In this paper, we show that existing
self-supervised methods do not perform well on depth estimation and propose a
gradient-based self-supervised learning algorithm with momentum contrastive
loss to help ConvNets extract the geometric information with unlabeled images.
As a result, the network can estimate the depth map accurately with a
relatively small amount of annotated data. To show that our method is
independent of the model structure, we evaluate our method with two different
monocular depth estimation algorithms. Our method outperforms the previous
state-of-the-art self-supervised learning algorithms and shows the efficiency
of labeled data in triple compared to random initialization on the NYU Depth v2
dataset.

Computer Vision and Pattern Recognition;Machine Learning;Robotics