DA3METRIC LARGE

---
license: apache-2.0
tags:

• depth-estimation

• computer-vision

• monocular-depth

• multi-view-geometry

• pose-estimation

Depth Anything 3: DA3METRIC-LARGE

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 # noqa: E501
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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

git clone https://github.com/ByteDance-Seed/depth-anything-3
cd depth-anything-3
pip install -e .

Basic Example

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

# 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. # 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:

@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

• 📄 Paper

• 💻 GitHub Repository

• 🤗 Hugging Face Demo

• 📚 Documentation

Authors

Haotong Lin · Sili Chen · Junhao Liew · Donny Y. Chen · Zhenyu Li · Guang Shi · Jiashi Feng · Bingyi Kang # noqa: E501