  Monocular depth estimation is scale-ambiguous, and thus requires scale
supervision to produce metric predictions. Even so, the resulting models will
be geometry-specific, with learned scales that cannot be directly transferred
across domains. Because of that, recent works focus instead on relative depth,
eschewing scale in favor of improved up-to-scale zero-shot transfer. In this
work we introduce ZeroDepth, a novel monocular depth estimation framework
capable of predicting metric scale for arbitrary test images from different
domains and camera parameters. This is achieved by (i) the use of input-level
geometric embeddings that enable the network to learn a scale prior over
objects; and (ii) decoupling the encoder and decoder stages, via a variational
latent representation that is conditioned on single frame information. We
evaluated ZeroDepth targeting both outdoor (KITTI, DDAD, nuScenes) and indoor
(NYUv2) benchmarks, and achieved a new state-of-the-art in both settings using
the same pre-trained model, outperforming methods that train on in-domain data
and require test-time scaling to produce metric estimates.
