  We propose a depth map inference system from monocular videos based on a
novel dataset for navigation that mimics aerial footage from gimbal stabilized
monocular camera in rigid scenes. Unlike most navigation datasets, the lack of
rotation implies an easier structure from motion problem which can be leveraged
for different kinds of tasks such as depth inference and obstacle avoidance. We
also propose an architecture for end-to-end depth inference with a fully
convolutional network. Results show that although tied to camera inner
parameters, the problem is locally solvable and leads to good quality depth
prediction.
