Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data
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.
65ade82bba78ec6b89e24c75
Lihe Yang
admin_assigned
2024-01-22T11:26:47.600Z
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Lihe Yang
user
LiheYoung
65ade82bba78ec6b89e24c76
Bingyi Kang
claimed_verified
2024-01-22T10:28:44.292Z
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Bingyi Kang
user
bykang
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Zilong Huang
admin_assigned
2024-01-22T11:27:53.983Z
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Zilong Huang
user
speedinghzl
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Xiaogang Xu
admin_assigned
2024-01-22T11:28:00.440Z
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Xiaogang Xu
user
xiaogang00
65ade82bba78ec6b89e24c79
Jiashi Feng
65ade82bba78ec6b89e24c7a
Hengshuang Zhao
Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data
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.
Depth Anything is a robust monocular depth estimation model built on a large-scale dataset with data augmentation and auxiliary supervision strategies, achieving state-of-the-art results on various datasets and enhancing depth-conditioned ControlNet.
monocular depth estimation data engine data augmentation auxiliary supervision pre-trained encoders zero-shot capabilities NYUv2 KITTI depth-conditioned ControlNet