  We introduce a convolutional neural network model for unsupervised learning
of depth and ego-motion from cylindrical panoramic video. Panoramic depth
estimation is an important technology for applications such as virtual reality,
3D modeling, and autonomous robotic navigation. In contrast to previous
approaches for applying convolutional neural networks to panoramic imagery, we
use the cylindrical panoramic projection which allows for the use of the
traditional CNN layers such as convolutional filters and max pooling without
modification. Our evaluation of synthetic and real data shows that unsupervised
learning of depth and ego-motion on cylindrical panoramic images can produce
high-quality depth maps and that an increased field-of-view improves ego-motion
estimation accuracy. We also introduce Headcam, a novel dataset of panoramic
video collected from a helmet-mounted camera while biking in an urban setting.
