Unsupervised Learning of Depth and Ego-Motion from Video

We present an unsupervised learning framework for the
task of monocular depth and camera motion estimation
from unstructured video sequences. In common with recent
work, we use an end-to-end learning approach
with view synthesis as the supervisory signal. In
contrast to the previous work, our method is completely unsupervised,
requiring only monocular video sequences for
training. Our method uses single-view depth and multi-view
pose networks, with a loss based on warping nearby
views to the target using the computed depth and pose. The
networks are thus coupled by the loss during training, but
can be applied independently at test time. Empirical evaluation
on the KITTI dataset demonstrates the effectiveness
of our approach: 1) monocular depth performs comparably
with supervised methods that use either ground-truth pose
or depth for training, and 2) pose estimation performs favorably
compared to established SLAM systems under comparable
input settings.

• Machine Perception

• Machine Intelligence