  Single-view depth estimation (SVDE) plays a crucial role in scene
understanding for AR applications, 3D modeling, and robotics, providing the
geometry of a scene based on a single image. Recent works have shown that a
successful solution strongly relies on the diversity and volume of training
data. This data can be sourced from stereo movies and photos. However, they do
not provide geometrically complete depth maps (as disparities contain unknown
shift value). Therefore, existing models trained on this data are not able to
recover correct 3D representations. Our work shows that a model trained on this
data along with conventional datasets can gain accuracy while predicting
correct scene geometry. Surprisingly, only a small portion of geometrically
correct depth maps are required to train a model that performs equally to a
model trained on the full geometrically correct dataset. After that, we train
computationally efficient models on a mixture of datasets using the proposed
method. Through quantitative comparison on completely unseen datasets and
qualitative comparison of 3D point clouds, we show that our model defines the
new state of the art in general-purpose SVDE.
