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
