271 lines
15 KiB
Markdown
271 lines
15 KiB
Markdown
<div align="center">
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<h1 style="border-bottom: none; margin-bottom: 0px ">Depth Anything 3: Recovering the Visual Space from Any Views</h1>
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<!-- <h2 style="border-top: none; margin-top: 3px;">Recovering the Visual Space from Any Views</h2> -->
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[**Haotong Lin**](https://haotongl.github.io/)<sup>*</sup> · [**Sili Chen**](https://github.com/SiliChen321)<sup>*</sup> · [**Jun Hao Liew**](https://liewjunhao.github.io/)<sup>*</sup> · [**Donny Y. Chen**](https://donydchen.github.io)<sup>*</sup> · [**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)
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<br>
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[**Bingyi Kang**](https://bingykang.github.io/)<sup>*†</sup>
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†project lead *Equal Contribution
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<a href="https://arxiv.org/abs/2511.10647"><img src='https://img.shields.io/badge/arXiv-Depth Anything 3-red' alt='Paper PDF'></a>
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<a href='https://depth-anything-3.github.io'><img src='https://img.shields.io/badge/Project_Page-Depth Anything 3-green' alt='Project Page'></a>
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<a href='https://huggingface.co/spaces/depth-anything/Depth-Anything-3'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Demo-blue'></a>
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<!-- <a href='https://huggingface.co/datasets/depth-anything/VGB'><img src='https://img.shields.io/badge/Benchmark-VisGeo-yellow' alt='Benchmark'></a> -->
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<!-- <a href='https://huggingface.co/datasets/depth-anything/data'><img src='https://img.shields.io/badge/Benchmark-xxx-yellow' alt='Data'></a> -->
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</div>
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This work presents **Depth Anything 3 (DA3)**, a model that predicts spatially consistent geometry from
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arbitrary visual inputs, with or without known camera poses.
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In pursuit of minimal modeling, DA3 yields two key insights:
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- 💎 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.
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🏆 DA3 significantly outperforms
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[DA2](https://github.com/DepthAnything/Depth-Anything-V2) for monocular depth estimation,
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and [VGGT](https://github.com/facebookresearch/vggt) for multi-view depth estimation and pose estimation.
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All models are trained exclusively on **public academic datasets**.
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<!-- <p align="center">
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<img src="assets/images/da3_teaser.png" alt="Depth Anything 3" width="100%">
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</p> -->
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<p align="center">
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<img src="assets/images/demo320-2.gif" alt="Depth Anything 3 - Left" width="70%">
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</p>
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<p align="center">
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<img src="assets/images/da3_radar.png" alt="Depth Anything 3" width="100%">
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</p>
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## 📰 News
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- **30-11-2025:** Add [`use_ray_pose`](#use-ray-pose) and [`ref_view_strategy`](docs/funcs/ref_view_strategy.md) (reference view selection for multi-view inputs).
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- **25-11-2025:** Add [Awesome DA3 Projects](#-awesome-da3-projects), a community-driven section featuring DA3-based applications.
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- **14-11-2025:** Paper, project page, code and models are all released.
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## ✨ Highlights
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### 🏆 Model Zoo
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We release three series of models, each tailored for specific use cases in visual geometry.
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- 🌟 **DA3 Main Series** (`DA3-Giant`, `DA3-Large`, `DA3-Base`, `DA3-Small`) These are our flagship foundation models, trained with a unified depth-ray representation. By varying the input configuration, a single model can perform a wide range of tasks:
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+ 🌊 **Monocular Depth Estimation**: Predicts a depth map from a single RGB image.
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+ 🌊 **Multi-View Depth Estimation**: Generates consistent depth maps from multiple images for high-quality fusion.
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+ 🎯 **Pose-Conditioned Depth Estimation**: Achieves superior depth consistency when camera poses are provided as input.
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+ 📷 **Camera Pose Estimation**: Estimates camera extrinsics and intrinsics from one or more images.
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+ 🟡 **3D Gaussian Estimation**: Directly predicts 3D Gaussians, enabling high-fidelity novel view synthesis.
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- 📐 **DA3 Metric Series** (`DA3Metric-Large`) A specialized model fine-tuned for metric depth estimation in monocular settings, ideal for applications requiring real-world scale.
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- 🔍 **DA3 Monocular Series** (`DA3Mono-Large`). A dedicated model for high-quality relative monocular depth estimation. Unlike disparity-based models (e.g., [Depth Anything 2](https://github.com/DepthAnything/Depth-Anything-V2)), it directly predicts depth, resulting in superior geometric accuracy.
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🔗 Leveraging these available models, we developed a **nested series** (`DA3Nested-Giant-Large`). This series combines a any-view giant model with a metric model to reconstruct visual geometry at a real-world metric scale.
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### 🛠️ Codebase Features
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Our repository is designed to be a powerful and user-friendly toolkit for both practical application and future research.
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- 🎨 **Interactive Web UI & Gallery**: Visualize model outputs and compare results with an easy-to-use Gradio-based web interface.
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- ⚡ **Flexible Command-Line Interface (CLI)**: Powerful and scriptable CLI for batch processing and integration into custom workflows.
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- 💾 **Multiple Export Formats**: Save your results in various formats, including `glb`, `npz`, depth images, `ply`, 3DGS videos, etc, to seamlessly connect with other tools.
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- 🔧 **Extensible and Modular Design**: The codebase is structured to facilitate future research and the integration of new models or functionalities.
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<!-- ### 🎯 Visual Geometry Benchmark
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We introduce a new benchmark to rigorously evaluate geometry prediction models on three key tasks: pose estimation, 3D reconstruction, and visual rendering (novel view synthesis) quality.
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- 🔄 **Broad Model Compatibility**: Our benchmark is designed to be versatile, supporting the evaluation of various models, including both monocular and multi-view depth estimation approaches.
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- 🔬 **Robust Evaluation Pipeline**: We provide a standardized pipeline featuring RANSAC-based pose alignment, TSDF fusion for dense reconstruction, and a principled view selection strategy for novel view synthesis.
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- 📊 **Standardized Metrics**: Performance is measured using established metrics: AUC for pose accuracy, F1-score and Chamfer Distance for reconstruction, and PSNR/SSIM/LPIPS for rendering quality.
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- 🌍 **Diverse and Challenging Datasets**: The benchmark spans a wide range of scenes from datasets like HiRoom, ETH3D, DTU, 7Scenes, ScanNet++, DL3DV, Tanks and Temples, and MegaDepth. -->
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## 🚀 Quick Start
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### 📦 Installation
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```bash
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pip install xformers torch\>=2 torchvision
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pip install -e . # Basic
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pip install --no-build-isolation git+https://github.com/nerfstudio-project/gsplat.git@0b4dddf04cb687367602c01196913cde6a743d70 # for gaussian head
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pip install -e ".[app]" # Gradio, python>=3.10
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pip install -e ".[all]" # ALL
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```
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For detailed model information, please refer to the [Model Cards](#-model-cards) section below.
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### 💻 Basic Usage
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```python
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import glob, os, torch
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from depth_anything_3.api import DepthAnything3
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device = torch.device("cuda")
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model = DepthAnything3.from_pretrained("depth-anything/DA3NESTED-GIANT-LARGE")
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model = model.to(device=device)
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example_path = "assets/examples/SOH"
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images = sorted(glob.glob(os.path.join(example_path, "*.png")))
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prediction = model.inference(
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images,
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)
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# prediction.processed_images : [N, H, W, 3] uint8 array
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print(prediction.processed_images.shape)
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# prediction.depth : [N, H, W] float32 array
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print(prediction.depth.shape)
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# prediction.conf : [N, H, W] float32 array
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print(prediction.conf.shape)
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# prediction.extrinsics : [N, 3, 4] float32 array # opencv w2c or colmap format
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print(prediction.extrinsics.shape)
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# prediction.intrinsics : [N, 3, 3] float32 array
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print(prediction.intrinsics.shape)
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```
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```bash
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export MODEL_DIR=depth-anything/DA3NESTED-GIANT-LARGE
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# This can be a Hugging Face repository or a local directory
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# If you encounter network issues, consider using the following mirror: export HF_ENDPOINT=https://hf-mirror.com
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# Alternatively, you can download the model directly from Hugging Face
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export GALLERY_DIR=workspace/gallery
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mkdir -p $GALLERY_DIR
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# CLI auto mode with backend reuse
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da3 backend --model-dir ${MODEL_DIR} --gallery-dir ${GALLERY_DIR} # Cache model to gpu
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da3 auto assets/examples/SOH \
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--export-format glb \
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--export-dir ${GALLERY_DIR}/TEST_BACKEND/SOH \
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--use-backend
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# CLI video processing with feature visualization
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da3 video assets/examples/robot_unitree.mp4 \
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--fps 15 \
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--use-backend \
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--export-dir ${GALLERY_DIR}/TEST_BACKEND/robo \
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--export-format glb-feat_vis \
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--feat-vis-fps 15 \
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--process-res-method lower_bound_resize \
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--export-feat "11,21,31"
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# CLI auto mode without backend reuse
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da3 auto assets/examples/SOH \
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--export-format glb \
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--export-dir ${GALLERY_DIR}/TEST_CLI/SOH \
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--model-dir ${MODEL_DIR}
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```
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The model architecture is defined in [`DepthAnything3Net`](src/depth_anything_3/model/da3.py), and specified with a Yaml config file located at [`src/depth_anything_3/configs`](src/depth_anything_3/configs). The input and output processing are handled by [`DepthAnything3`](src/depth_anything_3/api.py). To customize the model architecture, simply create a new config file (*e.g.*, `path/to/new/config`) as:
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```yaml
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__object__:
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path: depth_anything_3.model.da3
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name: DepthAnything3Net
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args: as_params
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net:
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__object__:
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path: depth_anything_3.model.dinov2.dinov2
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name: DinoV2
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args: as_params
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name: vitb
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out_layers: [5, 7, 9, 11]
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alt_start: 4
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qknorm_start: 4
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rope_start: 4
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cat_token: True
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head:
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__object__:
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path: depth_anything_3.model.dualdpt
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name: DualDPT
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args: as_params
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dim_in: &head_dim_in 1536
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output_dim: 2
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features: &head_features 128
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out_channels: &head_out_channels [96, 192, 384, 768]
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```
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Then, the model can be created with the following code snippet.
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```python
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from depth_anything_3.cfg import create_object, load_config
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Model = create_object(load_config("path/to/new/config"))
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```
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## 📚 Useful Documentation
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- 🖥️ [Command Line Interface](docs/CLI.md)
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- 📑 [Python API](docs/API.md)
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<!-- - 🏁 [Visual Geometry Benchmark](docs/BENCHMARK.md) -->
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## 🗂️ Model Cards
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Generally, you should observe that DA3-LARGE achieves comparable results to VGGT.
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The Nested series uses an Any-view model to estimate pose and depth, and a monocular metric depth estimator for scaling.
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| 🗃️ Model Name | 📏 Params | 📊 Rel. Depth | 📷 Pose Est. | 🧭 Pose Cond. | 🎨 GS | 📐 Met. Depth | ☁️ Sky Seg | 📄 License |
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|-------------------------------|-----------|---------------|--------------|---------------|-------|---------------|-----------|----------------|
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| **Nested** | | | | | | | | |
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| [DA3NESTED-GIANT-LARGE](https://huggingface.co/depth-anything/DA3NESTED-GIANT-LARGE) | 1.40B | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | CC BY-NC 4.0 |
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| **Any-view Model** | | | | | | | | |
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| [DA3-GIANT](https://huggingface.co/depth-anything/DA3-GIANT) | 1.15B | ✅ | ✅ | ✅ | ✅ | | | CC BY-NC 4.0 |
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| [DA3-LARGE](https://huggingface.co/depth-anything/DA3-LARGE) | 0.35B | ✅ | ✅ | ✅ | | | | CC BY-NC 4.0 |
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| [DA3-BASE](https://huggingface.co/depth-anything/DA3-BASE) | 0.12B | ✅ | ✅ | ✅ | | | | Apache 2.0 |
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| [DA3-SMALL](https://huggingface.co/depth-anything/DA3-SMALL) | 0.08B | ✅ | ✅ | ✅ | | | | Apache 2.0 |
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| | | | | | | | | |
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| **Monocular Metric Depth** | | | | | | | | |
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| [DA3METRIC-LARGE](https://huggingface.co/depth-anything/DA3METRIC-LARGE) | 0.35B | ✅ | | | | ✅ | ✅ | Apache 2.0 |
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| | | | | | | | | |
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| **Monocular Depth** | | | | | | | | |
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| [DA3MONO-LARGE](https://huggingface.co/depth-anything/DA3MONO-LARGE) | 0.35B | ✅ | | | | | ✅ | Apache 2.0 |
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## ❓ FAQ
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- **Monocular Metric Depth**: To obtain metric depth in meters from `DA3METRIC-LARGE`, use `metric_depth = focal * net_output / 300.`, where `focal` is the focal length in pixels (typically the average of fx and fy from the camera intrinsic matrix K). Note that the output from `DA3NESTED-GIANT-LARGE` is already in meters.
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- <a id="use-ray-pose"></a>**Ray Head (`use_ray_pose`)**: Our API and CLI support `use_ray_pose` arg, which means that the model will derive camera pose from ray head, which is generally slightly slower, but more accurate. Note that the default is `False` for faster inference speed.
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<details>
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<summary>AUC3 Results for DA3NESTED-GIANT-LARGE</summary>
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| Model | HiRoom | ETH3D | DTU | 7Scenes | ScanNet++ |
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|-------|------|-------|-----|---------|-----------|
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| `ray_head` | 84.4 | 52.6 | 93.9 | 29.5 | 89.4 |
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| `cam_head` | 80.3 | 48.4 | 94.1 | 28.5 | 85.0 |
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</details>
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- **Older GPUs without XFormers support**: See [Issue #11](https://github.com/ByteDance-Seed/Depth-Anything-3/issues/11). Thanks to [@S-Mahoney](https://github.com/S-Mahoney) for the solution!
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## 🏢 Awesome DA3 Projects
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A community-curated list of Depth Anything 3 integrations across 3D tools, creative pipelines, robotics, and web/VR viewers, including but not limited to these. You are welcome to submit your DA3-based project via PR, and we will review and feature it if applicable.
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- [DA3-blender](https://github.com/xy-gao/DA3-blender): Blender addon for DA3-based 3D reconstruction from a set of images.
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- [ComfyUI-DepthAnythingV3](https://github.com/PozzettiAndrea/ComfyUI-DepthAnythingV3): ComfyUI nodes for Depth Anything 3, supporting single/multi-view and video-consistent depth with optional point‑cloud export.
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- [DA3-ROS2-Wrapper](https://github.com/GerdsenAI/GerdsenAI-Depth-Anything-3-ROS2-Wrapper): Real-time DA3 depth in ROS2 with multi-camera support.
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- [VideoDepthViewer3D](https://github.com/amariichi/VideoDepthViewer3D): Streaming videos with DA3 metric depth to a Three.js/WebXR 3D viewer for VR/stereo playback.
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## 📝 Citations
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If you find Depth Anything 3 useful in your research or projects, please cite our work:
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```
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@article{depthanything3,
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title={Depth Anything 3: Recovering the visual space from any views},
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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:2511.10647},
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year={2025}
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}
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```
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