DA3NESTED GIANT LARGE 1.1 EU AI Act Compliance Profile

Depth Anything 3: DA3NESTED-GIANT-LARGE

# noqa: E501

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

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/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(predictio

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Tags

Library