Depth Anything V2
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.
666b9c7580a130166dbd9efc
Lihe Yang
claimed_verified
2024-06-14T07:23:21.471Z
65a3a0342548c41ad9f4e4e7
https://cdn-avatars.huggingface.co/v1/production/uploads/65a3a0342548c41ad9f4e4e7/80Yod9z7O95nC-Y0o-5UI.jpeg
Lihe Yang
user
LiheYoung
666b9c7580a130166dbd9efd
Bingyi Kang
claimed_verified
2024-06-14T07:23:30.766Z
647b5fef6a79fbf5e996c47c
https://cdn-avatars.huggingface.co/v1/production/uploads/647b5fef6a79fbf5e996c47c/IkSMnDsCY_CyEFCiMDuxe.jpeg
Bingyi Kang
user
bykang
666b9c7580a130166dbd9efe
Zilong Huang
claimed_verified
2025-05-14T07:38:03.094Z
63a430ec84a6a25c65bff20c
/avatars/3ab51214c491f28d8fe6b70c373d72aa.svg
Zilong Huang
user
speedinghzl
666b9c7580a130166dbd9eff
Zhen Zhao
666b9c7580a130166dbd9f00
Xiaogang Xu
666b9c7580a130166dbd9f01
Jiashi Feng
666b9c7580a130166dbd9f02
Hengshuang Zhao
Depth Anything V2
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.
Depth Anything V2 improves monocular depth estimation through synthetic images, larger teacher models, and pseudo-labeled real images, achieving better efficiency and accuracy than Stable Diffusion models.
monocular depth estimation synthetic images teacher model student models pseudo-labeled real images Stable Diffusion efficient accurate metric depth models evaluation benchmark precise annotations diverse scenes