  Monocular depth inference has gained tremendous attention from researchers in
recent years and remains as a promising replacement for expensive
time-of-flight sensors, but issues with scale acquisition and implementation
overhead still plague these systems. To this end, this work presents an
unsupervised learning framework that is able to predict at-scale depth maps and
egomotion, in addition to camera intrinsics, from a sequence of monocular
images via a single network. Our method incorporates both spatial and temporal
geometric constraints to resolve depth and pose scale factors, which are
enforced within the supervisory reconstruction loss functions at training time.
Only unlabeled stereo sequences are required for training the weights of our
single-network architecture, which reduces overall implementation overhead as
compared to previous methods. Our results demonstrate strong performance when
compared to the current state-of-the-art on multiple sequences of the KITTI
driving dataset and can provide faster training times with its reduced network
complexity.
