Depth Anything at Any Condition
We present Depth Anything at Any Condition (DepthAnything-AC), a foundation
monocular depth estimation (MDE) model capable of handling diverse
environmental conditions. Previous foundation MDE models achieve impressive
performance across general scenes but not perform well in complex open-world
environments that involve challenging conditions, such as illumination
variations, adverse weather, and sensor-induced distortions. To overcome the
challenges of data scarcity and the inability of generating high-quality
pseudo-labels from corrupted images, we propose an unsupervised consistency
regularization finetuning paradigm that requires only a relatively small amount
of unlabeled data. Furthermore, we propose the Spatial Distance Constraint to
explicitly enforce the model to learn patch-level relative relationships,
resulting in clearer semantic boundaries and more accurate details.
Experimental results demonstrate the zero-shot capabilities of DepthAnything-AC
across diverse benchmarks, including real-world adverse weather benchmarks,
synthetic corruption benchmarks, and general benchmarks.
  Project Page: https://ghost233lism.github.io/depthanything-AC-page
  Code: https://github.com/HVision-NKU/DepthAnythingAC
6865e04b8c83dab5f72d1e2d
Boyuan Sun
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2025-07-03T07:16:54.412Z
66ef2611fcc1c455f8dce832
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Boyuan Sun
user
BBBBCHAN
6865e04b8c83dab5f72d1e2e
Modi Jin
claimed_verified
2025-07-16T08:16:04.178Z
67232832391d8e0c4b2980f4
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Modi Jin
user
ghost233lism
6865e04b8c83dab5f72d1e2f
Bowen Yin
6865e04b8c83dab5f72d1e30
Qibin Hou
Depth Anything at Any Condition
We present Depth Anything at Any Condition (DepthAnything-AC), a foundation
monocular depth estimation (MDE) model capable of handling diverse
environmental conditions. Previous foundation MDE models achieve impressive
performance across general scenes but not perform well in complex open-world
environments that involve challenging conditions, such as illumination
variations, adverse weather, and sensor-induced distortions. To overcome the
challenges of data scarcity and the inability of generating high-quality
pseudo-labels from corrupted images, we propose an unsupervised consistency
regularization finetuning paradigm that requires only a relatively small amount
of unlabeled data. Furthermore, we propose the Spatial Distance Constraint to
explicitly enforce the model to learn patch-level relative relationships,
resulting in clearer semantic boundaries and more accurate details.
Experimental results demonstrate the zero-shot capabilities of DepthAnything-AC
across diverse benchmarks, including real-world adverse weather benchmarks,
synthetic corruption benchmarks, and general benchmarks.
  Project Page: https://ghost233lism.github.io/depthanything-AC-page
  Code: https://github.com/HVision-NKU/DepthAnythingAC
DepthAnything-AC is a monocular depth estimation model that uses unsupervised consistency regularization and spatial distance constraints to handle complex environmental conditions and achieve zero-shot performance across various benchmarks.
monocular depth estimation unsupervised consistency regularization spatial distance constraints zero-shot capabilities adverse weather synthetic corruption general benchmarks