  This work presents Depth Anything, a highly practical solution for robust
monocular depth estimation. Without pursuing novel technical modules, we aim to
build a simple yet powerful foundation model dealing with any images under any
circumstances. To this end, we scale up the dataset by designing a data engine
to collect and automatically annotate large-scale unlabeled data (~62M), which
significantly enlarges the data coverage and thus is able to reduce the
generalization error. We investigate two simple yet effective strategies that
make data scaling-up promising. First, a more challenging optimization target
is created by leveraging data augmentation tools. It compels the model to
actively seek extra visual knowledge and acquire robust representations.
Second, an auxiliary supervision is developed to enforce the model to inherit
rich semantic priors from pre-trained encoders. We evaluate its zero-shot
capabilities extensively, including six public datasets and randomly captured
photos. It demonstrates impressive generalization ability. Further, through
fine-tuning it with metric depth information from NYUv2 and KITTI, new SOTAs
are set. Our better depth model also results in a better depth-conditioned
ControlNet. Our models are released at
https://github.com/LiheYoung/Depth-Anything.
