| --- |
| license: apache-2.0 |
| tags: |
| - depth-estimation |
| - computer-vision |
| - monocular-depth |
| - multi-view-geometry |
| - pose-estimation |
| library_name: depth-anything-3 |
| pipeline_tag: depth-estimation |
| --- |
| |
| # Depth Anything 3: DA3METRIC-LARGE |
|
|
| <div align="center"> |
|
|
| [](https://depth-anything-3.github.io) |
| [](https://arxiv.org/abs/) |
| [](https://huggingface.co/spaces/depth-anything/Depth-Anything-3) # noqa: E501 |
| <!-- Benchmark badge removed as per request --> |
|
|
| </div> |
|
|
| ## Model Description |
|
|
| DA3 Metric Large model specialized for metric depth estimation in monocular settings, ideal for applications requiring real-world scale. Canonical metric depth; multiplying by focal length gives metric depth. |
|
|
| | Property | Value | |
| |----------|-------| |
| | **Model Series** | Monocular Metric Depth | |
| | **Parameters** | 0.35B | |
| | **License** | Apache 2.0 | |
|
|
|
|
|
|
| ## Capabilities |
|
|
| - ✅ Relative Depth |
| - ✅ Metric Depth |
| - ✅ Sky Segmentation |
|
|
| ## Quick Start |
|
|
| ### Installation |
|
|
| ```bash |
| git clone https://github.com/ByteDance-Seed/depth-anything-3 |
| cd depth-anything-3 |
| pip install -e . |
| ``` |
|
|
| ### Basic Example |
|
|
| ```python |
| import torch |
| from depth_anything_3.api import DepthAnything3 |
| |
| # Load model from Hugging Face Hub |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
| model = DepthAnything3.from_pretrained("depth-anything/da3metric-large") |
| model = model.to(device=device) |
| |
| # Run inference on images |
| images = ["image1.jpg", "image2.jpg"] # List of image paths, PIL Images, or numpy arrays |
| prediction = model.inference( |
| images, |
| export_dir="output", |
| export_format="glb" # Options: glb, npz, ply, mini_npz, gs_ply, gs_video |
| ) |
| |
| # Access results |
| print(prediction.depth.shape) # Depth maps: [N, H, W] float32 |
| print(prediction.conf.shape) # Confidence maps: [N, H, W] float32 |
| print(prediction.extrinsics.shape) # Camera poses (w2c): [N, 3, 4] float32 |
| print(prediction.intrinsics.shape) # Camera intrinsics: [N, 3, 3] float32 |
| ``` |
|
|
| ### Command Line Interface |
|
|
| ```bash |
| # Process images with auto mode |
| da3 auto path/to/images \ |
| --export-format glb \ |
| --export-dir output \ |
| --model-dir depth-anything/da3metric-large |
| |
| # Use backend for faster repeated inference |
| da3 backend --model-dir depth-anything/da3metric-large |
| da3 auto path/to/images --export-format glb --use-backend |
| ``` |
|
|
| ## Model Details |
|
|
| - **Developed by:** ByteDance Seed Team |
| - **Model Type:** Vision Transformer for Visual Geometry |
| - **Architecture:** Plain transformer with unified depth-ray representation |
| - **Training Data:** Public academic datasets only |
|
|
| ### Key Insights |
|
|
| 💎 A **single plain transformer** (e.g., vanilla DINO encoder) is sufficient as a backbone without architectural specialization. # noqa: E501 |
|
|
| ✨ A singular **depth-ray representation** obviates the need for complex multi-task learning. |
|
|
| ## Performance |
|
|
| 🏆 Depth Anything 3 significantly outperforms: |
| - **Depth Anything 2** for monocular depth estimation |
| - **VGGT** for multi-view depth estimation and pose estimation |
|
|
| For detailed benchmarks, please refer to our [paper](https://depth-anything-3.github.io). # noqa: E501 |
|
|
| ## Limitations |
|
|
| - The model is trained on academic datasets and may have limitations on certain domain-specific images # noqa: E501 |
| - Performance may vary depending on image quality, lighting conditions, and scene complexity |
|
|
|
|
| ## Citation |
|
|
| If you find Depth Anything 3 useful in your research or projects, please cite: |
|
|
| ```bibtex |
| @article{depthanything3, |
| title={Depth Anything 3: Recovering the visual space from any views}, |
| author={Haotong Lin and Sili Chen and Jun Hao Liew and Donny Y. Chen and Zhenyu Li and Guang Shi and Jiashi Feng and Bingyi Kang}, # noqa: E501 |
| journal={arXiv preprint arXiv:XXXX.XXXXX}, |
| year={2025} |
| } |
| ``` |
|
|
| ## Links |
|
|
| - 🏠 [Project Page](https://depth-anything-3.github.io) |
| - 📄 [Paper](https://arxiv.org/abs/) |
| - 💻 [GitHub Repository](https://github.com/ByteDance-Seed/depth-anything-3) |
| - 🤗 [Hugging Face Demo](https://huggingface.co/spaces/depth-anything/Depth-Anything-3) |
| - 📚 [Documentation](https://github.com/ByteDance-Seed/depth-anything-3#-useful-documentation) |
|
|
| ## Authors |
|
|
| [Haotong Lin](https://haotongl.github.io/) · [Sili Chen](https://github.com/SiliChen321) · [Junhao Liew](https://liewjunhao.github.io/) · [Donny Y. Chen](https://donydchen.github.io) · [Zhenyu Li](https://zhyever.github.io/) · [Guang Shi](https://scholar.google.com/citations?user=MjXxWbUAAAAJ&hl=en) · [Jiashi Feng](https://scholar.google.com.sg/citations?user=Q8iay0gAAAAJ&hl=en) · [Bingyi Kang](https://bingykang.github.io/) # noqa: E501 |
|
|
