  This work presents Depth Anything V2. Without pursuing fancy techniques, we
aim to reveal crucial findings to pave the way towards building a powerful
monocular depth estimation model. Notably, compared with V1, this version
produces much finer and more robust depth predictions through three key
practices: 1) replacing all labeled real images with synthetic images, 2)
scaling up the capacity of our teacher model, and 3) teaching student models
via the bridge of large-scale pseudo-labeled real images. Compared with the
latest models built on Stable Diffusion, our models are significantly more
efficient (more than 10x faster) and more accurate. We offer models of
different scales (ranging from 25M to 1.3B params) to support extensive
scenarios. Benefiting from their strong generalization capability, we fine-tune
them with metric depth labels to obtain our metric depth models. In addition to
our models, considering the limited diversity and frequent noise in current
test sets, we construct a versatile evaluation benchmark with precise
annotations and diverse scenes to facilitate future research.
