DepthAnythingAC

<div align="center"> <h1>Depth Anything At Any Condition</h1>

Boyuan Sun^1* · Modi Jin^1* · Bowen Yin¹ · Qibin Hou^1†

¹VCIP, Nankai University

*Equal contribution  †Corresponding author

English | 简体中文

<a href="https://arxiv.org/abs/2507.01634"><img src='https://img.shields.io/badge/Paper-2507.01634-red' alt='Paper PDF'></a> <a href="https://ghost233lism.github.io/depthanything-AC-page/ "><img src='https://img.shields.io/badge/Project-Page-green' alt='Project Page'></a> <a href='https://huggingface.co/ghost233lism/DepthAnything-AC'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Model-blue'></a> <a href='https://huggingface.co/spaces/ghost233lism/DepthAnything-AC'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Demo-orange' alt='Demo'></a> <a href='https://huggingface.co/papers/2507.01634'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Paper-yellow'></a>

</div>

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.

<div align="center"> <img src="assets/depthanything-AC-video.gif" alt="video" width="100%"> </div>

News

• 2025-07-03: 🚀 DepthAnything-AC was ranked #3 Paper of the Day on HuggingFace Daily Papers.
• 2025-07-03: 🔥 The paper of DepthAnything-AC is released.
• 2025-07-02: 🔥 The code of DepthAnything-AC is released.
• 2025-07-02: 🔥 The DepthAnything-AC model and evaluation benchmarks are released

TODO List

• [ ] Instructions for training dataset download and process.
• [ ] Jittor implementation of DepthAnything-AC.
• [ ] Longer and more comprehensive video demo.

Model Architecture

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 Hugging Face:

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

We also provide the DepthAnything-AC model on Google Drive: Download

Quick Inference

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

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