You can deploy this model and start earning money today!

depth-anything/
DA3NESTED-GIANT-LARGE

Name: DA3NESTED-GIANT-LARGE
Availability: InStock

Views

No views yet

Depth Anything 3: DA3NESTED-GIANT-LARGE

Model Description

DA3 Nested model combining the any-view Giant model with the metric Large model for metric-scale visual geometry reconstruction. This is our recommended model that combines all capabilities.

Property	Value
Model Series	Nested
Parameters	1.40B
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
✅ Metric Depth
✅ Sky Segmentation

Quick Start

Installation

pip install depth-anything-3

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/da3nested-giant-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/da3nested-giant-large

# Use backend for faster repeated inference
da3 backend --model-dir depth-anything/da3nested-giant-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.

✨ 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 and Visual Geometry Benchmark.

Limitations

The model is trained on academic datasets and may have limitations on certain domain-specific images
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},
  journal={arXiv preprint arXiv:XXXX.XXXXX},
  year={2025}
}

Authors

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

depth-anything/DA3NESTED-GIANT-LARGE