  The success of monocular depth estimation relies on large and diverse
training sets. Due to the challenges associated with acquiring dense
ground-truth depth across different environments at scale, a number of datasets
with distinct characteristics and biases have emerged. We develop tools that
enable mixing multiple datasets during training, even if their annotations are
incompatible. In particular, we propose a robust training objective that is
invariant to changes in depth range and scale, advocate the use of principled
multi-objective learning to combine data from different sources, and highlight
the importance of pretraining encoders on auxiliary tasks. Armed with these
tools, we experiment with five diverse training datasets, including a new,
massive data source: 3D films. To demonstrate the generalization power of our
approach we use zero-shot cross-dataset transfer}, i.e. we evaluate on datasets
that were not seen during training. The experiments confirm that mixing data
from complementary sources greatly improves monocular depth estimation. Our
approach clearly outperforms competing methods across diverse datasets, setting
a new state of the art for monocular depth estimation. Some results are shown
in the supplementary video at https://youtu.be/D46FzVyL9I8
