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.

本研究介绍了 Depth Anything V2，通过使用合成图像替代真实标注图像，提升教师模型的容量，并通过大规模伪标注真实图像来教授学生模型，成功构建了效率更高、准确性更好的多种规模深度估计模型，并构建了通用的评估基准，以促进未来的研究。