Nirman
depth-anything

depth-anything/DA3-GIANT-1.1

DA3 Giant model for multi-view depth estimation, camera pose estimation, and 3D Gaussian estimation. This is the flagship foundation model w...

Model Documentation

Depth Anything 3: DA3-GIANT





Project Page Paper Demo

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Model Description



DA3 Giant model for multi-view depth estimation, camera pose estimation, and 3D Gaussian estimation. This is the flagship foundation model with unified depth-ray representation.

| Property | Value | |----------|-------| | Model Series | Any-view Model | | Parameters | 1.15B | | License | CC BY-NC 4.0 |

⚠️ Non-commercial use only due to CC BY-NC 4.0 license.

Capabilities



  • ✅ Relative Depth
  • ✅ Pose Estimation
  • ✅ Pose Conditioning
  • ✅ 3D Gaussians


    • 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/da3-giant")
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/da3-giant

    Use backend for faster repeated inference
    
da3 backend --model-dir depth-anything/da3-giant
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.

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    ✨ 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.

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    Limitations



  • The model is trained on academic datasets and may have limitations on certain domain-specific images

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  • Performance may vary depending on image quality, lighting conditions, and scene complexity
  • ⚠️ Non-commercial use only due to CC BY-NC 4.0 license.


    • 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},  
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  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

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    Files & Weights

    FilenameSizeAction
    model.safetensors 5.05 GB