  Monocular depth predictors are typically trained on large-scale training sets
which are naturally biased w.r.t the distribution of camera poses. As a result,
trained predictors fail to make reliable depth predictions for testing examples
captured under uncommon camera poses. To address this issue, we propose two
novel techniques that exploit the camera pose during training and prediction.
First, we introduce a simple perspective-aware data augmentation that
synthesizes new training examples with more diverse views by perturbing the
existing ones in a geometrically consistent manner. Second, we propose a
conditional model that exploits the per-image camera pose as prior knowledge by
encoding it as a part of the input. We show that jointly applying the two
methods improves depth prediction on images captured under uncommon and even
never-before-seen camera poses. We show that our methods improve performance
when applied to a range of different predictor architectures. Lastly, we show
that explicitly encoding the camera pose distribution improves the
generalization performance of a synthetically trained depth predictor when
evaluated on real images.
