Depth Anything at Any Condition

Paper, Project Page, and Code

The model was presented in the paper Depth Anything at Any Condition. Project page: https://ghost233lism.github.io/depthanything-AC-page/ Code: https://github.com/HVision-NKU/DepthAnythingAC

Abstract

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.

Introduction

DepthAnything-AC is a robust monocular depth estimation (MDE) model fine-tuned from DepthAnything-V2, designed for zero-shot depth estimation under diverse and challenging environmental conditions, including low light, adverse weather, and sensor distortions.

To address the lack of high-quality annotations in corrupted scenes, we introduce a lightweight unsupervised consistency regularization framework that enables training on unlabeled data. Additionally, our proposed Spatial Distance Constraint helps the model learn patch-level geometric relationships, enhancing semantic boundaries and fine details.

Installation

Requirements

Python>=3.9
torch==2.3.0
torchvision==0.18.0
torchaudio==2.3.0
cuda==12.1

Setup

git clone https://github.com/HVision-NKU/DepthAnythingAC.git
cd DepthAnythingAC
conda create -n depth_anything_ac python=3.9
conda activate depth_anything_ac
pip install -r requirements.txt

Usage

Get Depth-Anything-AC Model

Download the pre-trained checkpoints from huggingface:

mkdir checkpoints
cd checkpoints

# (Optional) Using huggingface mirrors
export HF_ENDPOINT=https://hf-mirror.com

# download DepthAnything-AC model from huggingface
huggingface-cli download --resume-download ghost233lism/DepthAnything-AC --local-dir ghost233lism/DepthAnything-AC

Quick Inference

We provide quick inference scripts for single/batch image input in tools/. Please refer to the infer README for detailed information.

Training

We provide the full training process of DepthAnything-AC, including consistency regularization, spatial distance extraction/constraint and wide-used Affine-Invariant Loss Function.

Prepare your configuration in configs/ file and run:

bash tools/train.sh <num_gpu> <port>

Evaluation

We provide the direct evaluation for DA-2K, enhanced DA-2K, KITTI, NYU-D, Sintel, ETH3D, DIODE, NuScenes-Night, RobotCar-night, DS-rain/cloud/fog, KITTI-C benchmarks. You may refer to configs/ for more details.

bash tools/val.sh <num_gpu> <port> <dataset>

Results

Quantitative Results

DA-2K Multi-Condition Robustness Results

Quantitative results on the enhanced multi-condition DA-2K benchmark, including complex light and climate conditions. The evaluation metric is Accuracy ↑.

Method	Encoder	DA-2K	DA-2K dark	DA-2K fog	DA-2K snow	DA-2K blur
DynaDepth	ResNet	0.655	0.652	0.613	0.605	0.633
EC-Depth	ViT-S	0.753	0.732	0.724	0.713	0.701
STEPS	ResNet	0.577	0.587	0.581	0.561	0.577
RobustDepth	ViT-S	0.724	0.716	0.686	0.668	0.680
Weather-Depth	ViT-S	0.745	0.724	0.716	0.697	0.666
DepthPro	ViT-S	0.947	0.872	0.902	0.793	0.772
DepthAnything V1	ViT-S	0.884	0.859	0.836	0.880	0.821
DepthAnything V2	ViT-S	0.952	0.910	0.922	0.880	0.862
Depth Anything AC	ViT-S	0.953	0.923	0.929	0.892	0.880

Zero-shot Relative Depth Estimation on Real Complex Benchmarks

Zero-shot evaluation results on challenging real-world scenarios including night scenes, adverse weather conditions, and complex environmental factors. All results use ViT-S encoder.

Method	Encoder	NuScenes-night		RobotCar-night		DS-rain		DS-cloud		DS-fog
		AbsRel ↓	δ₁ ↑	AbsRel ↓	δ₁ ↑	AbsRel ↓	δ₁ ↑	AbsRel ↓	δ₁ ↑	AbsRel ↓	δ₁ ↑
DynaDepth	ResNet	0.381	0.394	0.512	0.294	0.239	0.606	0.172	0.608	0.144	0.901
EC-Depth	ViT-S	0.243	0.623	0.228	0.552	0.155	0.766	0.158	0.767	0.109	0.861
STEPS	ResNet	0.252	0.588	0.350	0.367	0.301	0.480	0.252	0.588	0.216	0.641
RobustDepth	ViT-S	0.260	0.597	0.311	0.521	0.167	0.755	0.168	0.775	0.105	0.882
Weather-Depth	ViT-S	-	-	-	-	0.158	0.764	0.160	0.767	0.105	0.879
Syn2Real	ViT-S	-	-	-	-	0.171	0.729	-	-	0.128	0.845
DepthPro	ViT-S	0.218	0.669	0.237	0.534	0.124	0.841	0.158	0.779	0.102	0.892
DepthAnything V1	ViT-S	0.232	0.679	0.239	0.518	0.133	0.819	0.150	0.801	0.098	0.891
DepthAnything V2	ViT-S	0.200	0.725	0.239	0.518	0.125	0.840	0.151	0.798	0.103	0.890
Depth Anything AC	ViT-S	0.198	0.727	0.227	0.555	0.125	0.840	0.149	0.801	0.103	0.889

Bold: Best performance, Underlined: Second best performance. NuScenes-night and RobotCar-night represent nighttime driving scenarios. DS-rain, DS-cloud, and DS-fog are DrivingStereo weather variation datasets.

Zero-shot Relative Depth Estimation on Synthetic KITTI-C Benchmarks

Zero-shot evaluation results on synthetic KITTI-C corruption benchmarks, testing robustness against various image degradations and corruptions.

Method	Encoder	Dark		Snow		Motion		Gaussian
		AbsRel ↓	δ₁ ↑	AbsRel ↓	δ₁ ↑	AbsRel ↓	δ₁ ↑	AbsRel ↓	δ₁ ↑
DynaDepth	ResNet	0.163	0.752	0.338	0.393	0.234	0.609	0.274	0.501
STEPS	ResNet	0.230	0.631	0.242	0.622	0.291	0.508	0.204	0.692
DepthPro	ViT-S	0.145	0.793	0.197	0.685	0.170	0.746	0.170	0.745
DepthAnything V2	ViT-S	0.130	0.832	0.115	0.872	0.127	0.840	0.157	0.785
Depth Anything AC	ViT-S	0.130	0.834	0.114	0.873	0.126	0.841	0.153	0.793

KITTI-C includes synthetic corruptions: Dark (low-light conditions), Snow (weather simulation), Motion (motion blur), and Gaussian (noise corruption).

Citation

If you find this work useful, please consider citing:

@article{sun2025depth,
  title={Depth Anything at Any Condition},
  author={Sun, Boyuan and Modi Jin and Bowen Yin and Hou, Qibin},
  journal={arXiv preprint arXiv:2507.01634},
  year={2025}
}

License

This code is licensed under the Creative Commons Attribution-NonCommercial 4.0 International for non-commercial use only. Please note that any commercial use of this code requires formal permission prior to use.

Contact

For technical questions, please contact sbysbysby123[AT]gmail.com or jin_modi[AT]mail.nankai.edu.cn

For commercial licensing, please contact andrewhoux[AT]gmail.com.

Acknowledgements

We thank the authors of DepthAnything and DepthAnything V2 for their foundational work. We also acknowledge DINOv2 for the robust visual encoder, CorrMatch for their codebase, and RoboDepth for their contributions.

ghost233lism
/

DepthAnything-AC